Consumers Perception towards Online Shopping: A case study on Malaysian Market

Consumers Perception towards Online Shopping: A case study on Malaysian Market 1. Introduction The emergence of the internet has created opportunities for firms to stay competitive by providing customers with a convenient, faster and cheaper way to make purchases. JARING was the first to introduce internet service in Malaysia back in 1990 (Harn el at., 2006). A study by International Data Corporation (2005) indicated that the future of online shopping in Malaysia is bright and promising. The Government of Malaysia introduced business e-commerce in early 1998 (Kamarohim, 2003).Therefore, the attitudinal effect of online shopping by the use of the internet has expected little direct research attentions so far. Electronic commerce has become more important because marketer has some basis to market their product and services to the internet channel. It is more effectively and profitability marketer can serve their customer if they concern and understand their customer needs and wants. As an illustration, it is important to investigate the perception of online shoppers and products offered in order to present as a major player in Malaysia online shopping market. 1. Literature Review Nowadays, internet is not only for platform in networking, however, it also as a medium to bond together for almost businesses with its customers (Delafrooz et al., 2009). E-commerce is also called online shopping. It means running the whole process of business electronically using the internet (Chaffey et al., 2006). Online Shopping is a process where customers go through when they decide to purchase via the internet. For online shopping retailer, in order to ensure the achievement of business e-commerce, it is significant to establish customer needs and wants (Chaffey et al., 2006). Furthermore, online shopping is a new business strategy in Asia country (MasterCard, 2008). According to research done by ACNielsen, total of internet user is increasing time by time, 627 million people in the world was used internet as a medium to shopping (ACNielsen, 2007). Research done by Joines et al. (2003) and Houque et al. (2006) had come out with the same judgment which is the internet user has continuously growing and give impact to the online purchase on the internet. This result shows an opportunity arrived from the technology factor and can be as a benefit to company if they know how to use these chances. The main objective of this research is to examine consumers perception towards online shopping with a specific focus on convenience and security on consumer market in Malaysia. Studies of this nature conducted quite extensively in developed countries but in a developing country context is very limited. This gap was addressed with an empirical case study conducted in Malaysia. Despite high potential of online shopping in Malaysia, there is still lack of convenience and security issues on consumer market. From several reviews on research paper (Changchit, 2006; Delafrooz et al., 2009; Lee et al., 2010) many businesses has decided to penetrate internet commerce market, and yet, the achievement of particular business is different base on how they attract and convince their prospect. In further detail, research from Forrester (2006) figured out e-commerce market will attain $228 billion in 2007, and increasing $30 billion for every year until 2009. The market also will have accounted for $316 billion in sales by 2010 for retail sales. It mean, from the internet opportunity, entire online shopping industry has drastic changes and can give a huge profit for a marketer. Huang et al. (2011) suggest self- attitude and past experience give a significant impact in influencing buying decision process. Image interactive technology and experimenting appearance positively impact on buying attitude as well as decrease perceived risk toward the

e-commerce retailer (Lee et al., 2010). Other research done by Pentina et al. (2011) confirmed the increasing sales and visitor for online store based on the mediating of browser satisfaction. Internet retailing is one of the fastest growing sectors in the UK, and is having significant effects on traditional retail provision (Gunawan et al., 2008). According to Interactive Media Retail Group, internet sales have continued to rise from 14.5 billion in 2004 to around 26 billion, in 2006, which represents 10 per cent of total retail sales in the UK (Gunawan et al., 2008). The actual number of internet shoppers has also grown; in 2006, approximately 26 million, over half of UK adults, bought goods via the internet (Gunawan et al., 2008). In the case of China, the development of e-commerce faces several difficulties (Yu, 2006). Internet penetration among households in China lags far behind developed countries because the access price is out of reach for many. The cost of access is much higher than it is in the USA. A study by International Data Corporation Asia-Pacific, indicates that the future forecast for online shopping in Malaysia looks bright and promising (Chua et al., 2006). This is plausibly because internet commerce is still relatively new and there are no hard and fast rules to follow, with no tried and tested business model to imitate (Chua et al., 2006). It is important for Malaysian firms to have a good understanding of the marketplace for their products and their target customers before engaging in online retailing (Chua et al., 2006). In Malaysia, online shopping gets more attention from customers. However, each of them has different perception toward purchasing via the internet. This situation occurs because purchasing from internet give many benefits to customers such as shopping from their place; reduce cost of transportation, wide variety of choices and so on. Online shoppers try and adopt internet shopping environment base on convenience of the website retailer (Lee et al., 2010). They concluded convenience of online shopping include five main issues which are time spent, flexibility, information opportunities and less effort of going to physical shop. Madleberger (2006) recent study has shown that convenience is the main factor to influence shopper made online purchased. Therefore, consumer was asked about why they purchased through the internet, and researchers found that convenience was the key point of that problem statement (Madleberger, 2006; Chen et al., 2002; Torkzadeh et al., 2002; Becerra et al., 2011). However, other researcher concludes that convenience is not a big issue in stimulate customer decision making rather than risk perception (Chang et al., 2008). Security is a privacy policy to protect such personal information from being used by other party which include inside or outside the organizations (Flaviaan et al., 2006). Security concern is also one of the key points that internet users were not purchased over the e- commerce website (Flaviaan et al., 2006). Recently, as many third party companies provide security software and maintenance to the e-commerce website to protect those websites from becomes a hacker s victim, online marketer try to provide secure service to their customer in order to decrease risk perception from the customer. Online marketers try to provide information of internet security and focused on potential risks to internet user who used credit card to make online purchases, payment scams are major treat to electronic merchants (Liu et al., 2005). In Malaysia, however, despite the phenomenal growth in online retailing, a clear understanding of the facilitators of online purchase intention of customers is still lacking due largely to little research done within the Malaysia context. It is important for researchers and practitioners especially those who run/manage online businesses to be aware of the factors that encourage customers to repurchase from an online store. Therefore, this paper aims to examine to find out consumers perception towards online shopping with a specific focus on convenience and security on consumer market in Malaysia.

3. Originality, Research Objectives and Hypothesis The main objective of this case study is to examine consumers perception towards online shopping in Malaysian market. It shows a big gap between Malaysia e-commerce market and western countries (Changchit, 2006; Delafrooz et al., 2009; Lee et al., 2010) such as Europe where online shopping in Malaysia is not comprehensive compare to those countries. By looking at this gap, this study attempts to find the key point of reason why people purchase on the internet and what the key factors influence them to make purchases. In Malaysia, online shopping gets more attention from customers (Huang et al., 2006). However, each of them has different perception toward purchasing via the internet. This situation occurs because purchasing from internet give many benefits to customers such as shopping from their place, reduce cost of transportation, wide variety of choices and so on (Changchit, 2006; Delafrooz et al., 2009; Lee et al., 2010). The objective for this research is: 1) To examine the factors why people perceived online shopping. 2) To investigate the relationship between antecedents (convenience and safety) and buying decision process over the internet. Hypothesis is a presumption which is provisionally accepted in order to expose certain problem and to present assistance for further research and study (Saunders et al., 2007). Each hypothesis can be verified as correct or wrong. Based on the literature review, the researcher proposes following hypotheses; H1. Convenience has a positive influence on customer online shopping intentions H2. Security has a positive influence on customer online shopping intentions 4. Methodology: The research methods include the survey questionnaire design, the sample and methods of data analysis. The questionnaire for this study separated into two segments thus section A and B. Section A consists on the demographic profile such as gender, age, marital status, income, races and employment status. Section B is made up with main research questions. Introduction, literature review and this section is focused on the key that theoretical framework namely convenience and security. Convenience was adapted from (Lee et al., 2010; Chang et al., 2008; Madleberger, 2006; Chen et al., 2002; Torkzadeh et al., 2002; Becerra et al., 2011), security was adapted from (Flaviaan et al., 2006; Liu et al., 2005). The following figure is about hypotheses and its related questions. Figure 1: Relationship between variables and hypotheses Figure 2 explains about scale measurements. The extent of each variable was based on fivepoint Likert scales which scale points from 1-strongly disagree to 5-strongly agree. Several researchers have used the same measurement in their paper because the reliability of Likert scales tends to be good and it gives a wide choices answer to respondents (Houque et al., 2006; Changchit, 2006; Goldsmith et al., 2004). Figure 2: Scale measurement The main independent variables in this case study were responding to the various theoretical framework of the buying decision process as shown in figure 3. It means all the antecedent in

above figure is independent variables that can affect on consumer decision making. However, dependent variable is referred to the customer decision whether buy the product through the internet or perceive physical shop. The sample method for this research is based on snowball sampling method. This was to ensure that the participants have used the internet to purchase a product or service. Since, this case study was interested in participants willingness and ability to repurchase products/services online, it was considered reasonable to collect data from those who have prior experience in buying products or services online in line with the key informant technique (Ndubisi, 2011). The key informant method was used and only customers with online shopping experience were requested to respond to the questions. Key informants are viewed as appropriate respondents if appropriate selection procedures are used (John and Reve, 1982). Thus, using guidelines on selecting key respondents from previous research (Campbell, 1955), key informants were screened and chosen on the basis of their knowledge of the research issues, their experience with online shopping, and willingness to respond. A sample size of 40 people was used for this research. This study used a self-administered survey technique to distribute the questionnaire to acquire respondent responses to the survey. The significance method used in order to get appropriate respondents to answer the question. The respondents screened and chosen on the basis of their knowledge of the related issues; this is based on guidelines to select key respondents who show willingness to give the respond (John and Reve, 1982). Figure 3: Reliability Test for Each Variable Figure 3 presents reliability test for each variables in the questionnaire. Overall reliability of scales adopted in this survey questionnaire was 0.869 which shows very good consistency among the scales (Law and Bai, 2008). However, the reliability of individual variables seems different from each other and varied from 0.810 to 0.928. Then, this survey question can be reliable to examine what is the key factor of online shopping intentions. The relationship between variables and buying decision over the internet, correlation analysis was applied to predict the correlation among them. 5. Findings, Analysis and Discussion: 5.1 Demographic Profile of the Respondent Figure 4: Respondents profile The demographic characteristics of the study are presented in figure 4. The female respondents have the highest frequency (57.5%) while male respondents are only 42.5%. It can be clarified that there might be female respondent were willing to participate in the survey. This result meets the research done by Kamarohim (2003) and Lee et al. (2010) that female customers are more likely to purchase online compare to male respondents. As for marital status characteristics, both single and married have the same frequency which 50%. For marital status, it does not match with research done by Lee et al. (2010) that they concluded more respondents who are single (60.8%) while the rest is married. Next is age variable. From the findings, it shows respondents who are below 20 have the highest frequency (27.5%), 22.5% for both group 20-30 and 31-40, 15% for group 41-50, however the group above 50 has the lowest frequency (12.5%). This can be concluded teenagers are the major group who involve in online shopping. Lee et al. (2010) has different argument that group age 20-30 is has made major involvement in online shopping activity.

Then, for the race variable, Malay respondents are the highest (47.5%) who participate in this survey, Chinese respondents represent 25%, Indian respondents are 17.5% while follow by Other only 10%. Analysis done by Kamarohim (2003) shows that 80.6% from 160 respondents were Malay, 11.9% were Chinese, 5.0% were Indian and the rest of them were Other. So this paper matches with latter researcher in terms of race/ethnicity. The next demographic analysis is education levels. It can be summarized that respondent who have at least SPM/O-level were 55% from 40 respondents, 22.5% respondents have STPM/A-level, 7.5% for Diploma and another 15% were Postgraduate such as Master Degree or PhD. Respondents who have monthly income below RM 2,000 represents 45% of the respondents, 27.5% respondents who have monthly income RM 2,000 to RM 4,000, 20% of respondents belong to group RM 6,001 to RM 8,000, 5% respondents is in the group of RM 4,001 to RM 6,000, while the lowest percentage is the group RM 8,001 to RM 10,000 (2.5%). Lee et al. (2010) have different analysis where respondents who belong to range RM 2,000 to RM 4,000 have the highest frequency. (57.5%) follow by student (27.5%), and then self-employed respondents are 10% of the respondents while homemaker respondents are only 5%. Kamarohim (2003) and Lee et al. (2010) have come out with the same analysis where employed respondents are the highest group who involve in online shopping activity. 5.2 Correlation Analysis Correlation analysis was used to analyse the relationship between all variables (Saunders et al., 2007). Figure 5: Correlation Analysis between Antecedents and Online Shopping Intentions Seven variables were analysed by using correlation analysis. 44 correlation coefficients were produced as shown in the figure 7. These values point out the result for hypotheses which have been determined in the literature review section. The following summary explained the hypotheses results. As for conclusion, correlation analysis has summarized as following table: Figure 6: Correlation analysis summary Figure 6 illustrates the relationship between variables and online shopping intentions. From the summary, it can be concluded that price is the most correlated factor with online shopping intentions. The value is 0.551 which signified a strong positive relationship. From this findings, it can be summarized that customer will consider the convenience and security before involve in online shopping. They perceived online shopping because it is easy to make comparison and search for a discounted price. Analysis of Convenience and Online Shopping Intentions: As presented in the correlation figure, the value between convenience factors and online shopping intentions is 0.488. It means there is a weak relationship between convenience and online shopping intentions. This finding confirmed that hypothesis is accepted and it matches with Lee et al. (2010), Medleberger (2006) and Chen et al. (2002). Convenience is a significant factor why customer perceives online shopping. Analysis of Security and Online Shopping Intentions: The correlation between security concern and online shopping intentions is 0.225 which explains a weak positive relationship between the variables. The hypothesis for security and online shopping intentions is accepted. This analysis agrees with Flaviaan et al. (2006) and

Liu et al. (2005). They conclude that such securities on the website would affect on customer purchase behavior. 6. Conclusion This case study contributes to addressing the limited research conducted in Malaysia. The findings test variables established in developed countries using Malaysian sample to find out how these factors influence consumers perception towards online shopping. The findings of this case study suggest that global companies could find high potential market where internet users are growing high among the population. In addition, findings also suggest that consumers in Malaysia have intentions to purchase from the online if the market is more convenient and secure. Such opportunities for global companies are prevalent while domestic companies facing difficulties in setting up online business. Therefore, global companies could achieve competitiveness through early entering advantage into Malaysian market. It is expected that a further study could include the following: firstly, by employing a larger sample size using stratified random sampling. Using enlarge stratified sampling across the population would enhance the generalisation of the hypothesis. Secondly, an exploratory factor analysis could be used to analysis the interrelationships among variables into few dominant factors. Finally, further studies could explore additional antecedents of online shopping in other developing country context. References ACNielsen (2005), Global consumer attitudes towards online shopping [online].available at: http://www2.acnielsen.com/reports/document/2005_cc_onlineshopping.pdf. [Accessed 2 November 2011] ACNielsen (2007), Seek and You Shall Buy. Entertainment and Travel [online]. Available at: http://www.acnielsen.com/news/20051019.html [Accessed 3 November 2011] Brassington, F. and Pettitt, S. (2000), Principle of Marketing, 2 nd Ed, Edinburgh Gate, Pearson Education Limited. Campbell, D. (1955), The informant in quantitative research, American Journal of Sociological, Vol. 60, January, pp. 110-33. Chaffey, D. Ellis-Chadwick, F. Mayer, R. and Johnston, K. (2006), Internet Marketing, Strategy, Implementation and Practice, 3 rd Pearson Education Limited, England. Chang, H.H. and Chen, S.W. (2008), The impact of online store environment cues on purchase intention, trust and perceived risk as a mediator, Online Information Review, Vol. 32, No. 6, pp. 818-41. Changchit, C. (2006), Consumer Perception on Online Shopping, Issues In Information System, Vol. 4, No. 2, pp. 177-181, 2006. Chua, A.P.H. Khatibi, A. Ismail, H.B. (2006), E-Commerce: a study on online shopping in Malaysia, J. Soc. Sci., Vol. 13, No. 3, pp. 231-242. Connoly, P. (2007), Quantitative Data Analysis in Education: A critical introduction using SPSS, 1 st Ed, Routledge, NY. Constantinides, E. (2004) Influencing the online consumer s behaviour: the Web experience, Internet Research, Vol. 14, No. 2, pp. 111-126, 2004. Delafrooz,N. Pain, H.L. Haron, S.A. Sidin, S.N. And Khatibi, A. (2009), Factors affectings attitude toward online shopping, African Journal of Business Management, Vol. 3, No. 5, pp. 200-209, May 2099.

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Yates, R. (2005), Website accessibility and usability: towards more functional sites for all, Campus-Wide Information Systems, Vol. 22, No. 4,pp. 180-88. Yu, J. (2006), Marketing to Chinese consumers on the internet, Marketing Intelligence & Planning, Vol. 24 No. 4, pp. 380-92 Appendices Figure 1: Relationship between variables and hypotheses Variables Questions Consumer demographic characteristics Demography 1-7 Online shopping intentions Attitude 8-10 H1. Price perception will have a positive influence on customer online shopping intentions H2. Convenience will have a positive influence on customer online shopping intentions H3. Security will have a positive influence on customer online shopping intentions H4. Risk will have a positive influence on customer online shopping intentions H5. Shopping experience will have a positive influence on customer online shopping intentions H6. Time will have a positive influence on customer online shopping intentions Price 11-13 Convenience 14-16 Security 17-19 Risk 20-22 Shopping 23-25 experience Time 26-28 Figure 2: Scale measurement

Figure 3: Reliability Test for Each Variable Component Price.909 Convenience.884 Security.865 Risk.909 Shopping_Experience.810 Time.928 Online_Shopping_Intentions.782 Average.869 Cronbach's Alpha Figure 4: Respondents profile Items Categories Frequency Percentage (%) Gender Male 17 42.5 Female 23 57.5 Marital Status Single 20 50 Married 20 50 Age Below 20 11 27.5 20-30 9 22.5 31-40 9 22.5 41-50 6 15 Above 50 5 12.5 Race Malay 19 47.5 Chinese 10 25 Indian 7 17.5 Other 4 10 Education Level SPM/O-level 22 55 STPM/A-level 9 22.5 Diploma 3 7.5 Postgraduate 6 15 Monthly Income Below RM 2,000 18 45 RM 2,000 to RM 11 27.5 4,000 RM 4,001 to RM 2 5

6,000 RM 6,001 to RM 8,000 RM 8,001 to RM 10,00 8 20 1 2.5 Job Status Employed 23 57.5 Self-employed 4 10 Homemaker 2 5 Student 11 27.5 Figure 5: Correlation Analysis between Antecedents and Online Shopping Intentions Price Convenience Security Risk Shopping_Experience Time Online_Shopping_Intenti ons Price Correlations Convenienc e Security Risk **. Correlation is significant at the 0.01 level (2-tailed) *. Correlation is significant at the 0.005 level (2-tailed) a. Listwise N=40 Shopping_ Experience Time Online_Sho pping_inten tions 1.825.610.671.691.530.551.000.000.000.000.000.000.825 1.560.500.644.643.488.000.000.001.000.000.001.610.560 1.890.944.797.225.000.000.000.000.000.162.671.500.890 1.813.746.269.000.001.000.000.000.093.691.644.944.813 1.658.324.000.000.000.000.000.041.530.643.797.746.658 1.117.000.000.000.000.000.473.551.488.225.269.324.117 1.000.001.162.093.041.473 Figure 6: Correlation analysis summary Hypotheses Result H1. Convenience will have a positive influence on customer online shopping intentions H2. Security will have a positive influence on customer online shopping intentions Accepted Accepted