Comparitive Study of Shopping

International Journal of Humanities and Social Science

Vol. 2 No. 4 [Special Issue – February 2012]

FACTORS INFLUENCING CONSUMERS’ ATTITUDE TOWARDS E-COMMERCE PURCHASES THROUGH ONLINE SHOPPING Zuroni Md Jusoh Goh Hai Ling Centre of Excellent for Sustainable Consumption Research Department of Resource Management and Consumer Studies Faculty of Human Ecology Universiti Putra Malaysia 43400 Serdang, Selangor Malaysia. Abstract
Online shopping is the process of buying goods and services from merchants who sell on the internet. Shoppers can visit web stores from the comfort of their homes and shop as they sit in front of the computer. The main purpose of this study is to determine the factors influencing consumers’ attitude towards e-commerce purchases through online shopping. The study also investigate how socio-demographic (age, income and occupation), pattern of online buying (types of goods, e-commerce experience and hours use on internet) and purchase perception (product perception, customers’ service and consumers’ risk) affect consumers’ attitude towards online shopping. Convenience sampling method was conducted in this study and the sample comparison of 100 respondents in Taman Tawas Permai, Ipoh. Data were collected via self-administered questionnaire which contains 15 questions in Part A (respondents’ background and their pattern of using internet and online buying), 34 questions in Part B (attitude towards online purchase) and 36 questions in Part C (purchase perception towards online shopping). One-way ANOVA were used to assess the differences between independent variable such as age, income, occupation and pattern of online buying (type of goods) and dependant variable such as attitude towards online shopping. The findings revealed that there is no significant difference in attitude towards online shopping among age group (F = 1.020, p < 0.05) but there is a significant difference in attitude towards online shopping among income group (F = 0.556, p > 0.05). The research finding also showed that there is no significant difference in attitude towards online shopping among occupation group (F = 1.607, p < 0.05) and types of goods group (F = 1.384, p < 0.05). Pearson’s correlation were used to assess the relationship between independent variable such as e-commerce experience, hours spent on internet, product perception, customers’ service and consumers’ risk and dependant variable such as attitude towards online shopping. The findings revealed that there is a significant relationship between e-commerce experience and attitude towards online shopping among the respondents (r = -0.236**, p < 0.05). However, there is no significant relationship between hours spent on internet and attitude towards online shopping among the respondents (r = 0.106, p > 0.05). This study also indicated that there is a significant relationship between product perception and attitude towards online shopping among the respondents (r = 0.471**, p < 0.01) and there is also a significant relationship between customers’ service and attitude towards online shopping among the respondents (r = 0.459**, p < 0.01). Lastly, this result showed that there is no significant relationship between consumers’ risk and attitude towards online shopping among the respondents (r = 0.153, p > 0.05). Further study should explore other factors that influencing consumers’ attitude towards e-commerce purchases through online shopping with a broader range of population and high representative sampling method.
INTRODUCTION

1.1 Definition of online shopping Online shopping is defined as the process a customer takes to purchase a service or product over the internet. In other words, a consumer may at his or her leisure buy from the comfort of their own home products from an online store. This concept was first demonstrated before the World Wide Web (WWW) was in use with real time transaction processed from a domestic television. The technology used was called Videotext and was first demonstrated in 1979 by M. Aldrick who designed and installed systems in the United Kingdom. 223

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By 1990 T. Berners-Lee created the first WWW server and browser and by 1995 Amazon expanded its online shopping experiences (Parker-Hall, 2009). 1.2 The benefits of online shopping From the buyer‟s perspective also e-commerce offers a lot of tangible advantages. For example, reduction in buyer‟s sorting out time, better buyer decisions, less time is spent in resolving invoice and order discrepancies and finally increased opportunities for buying alternative products. Moreover, consumers can enjoy online shopping for 24 hour per day. This is because e-commerce is open for 365 days and never close even for a minute. Ecommerce also expanded geographic reach because consumers can purchase any goods and services anytime at everywhere. Hence, online shopping is more environmental friendly compare to purchase in store because consumers can just fulfill his desires just with a click of mouse without going out from house by taking any transportation.
OBJECTIVE

General Objective Generally, this paper is to identify the attitude of online shoppers towards online shopping. Specific Objective 1. To investigate how socio-demographic (age, income and occupation) affect consumers‟ attitude towards online shopping. 2. To probe how the pattern of online buying (types of goods, e-commerce experience and hours use on internet) influence consumers‟ attitude towards online shopping. 3. To examine how purchase perception (product perception, customer service and consumer risk) influence consumers‟ attitude towards online shopping.
HYPOTHESIS

Ho1: There is no significant difference between age and attitude towards online shopping. Ho2: There is no significant difference between income and attitude towards online shopping. Ho3: There is no significant difference between occupation and attitude towards online shopping. Ho4: There is no significant difference between pattern of online buying (types of goods) and attitude towards online shopping. Ho5: There is no significant relationship between e-commerce experience and attitude towards online shopping. Ho6: There is no significant relationship between hours spent on internet and attitude towards online shopping. Ho7: There is no significant relationship between product perception and attitude towards online shopping. Ho8: There is no significant relationship between customer service and attitude towards online shopping. Ho9: There is no significant relationship between consumers‟ risk and attitude towards online shopping.
LITERATURE REVIEW

1 Attitude Several researchers have carried out studies in their effort to examine the factors influencing consumers‟ attitude and perception to make e-commerce purchases through online shopping. Attitudes toward online shopping are defined as a consumer‟s positive or negative feelings related to accomplishing the purchasing behavior on the internet (Chiu et al., 2005; Schlosser, 2003). Buying trends and internet adoption indications have been seen as the overall electronic commerce value in Malaysia rising from US$18 million in 1998 to US$87.3 million in 1999 (Mohd Suki et al., 2006). In order to investigate consumers‟ attitudes, we need to know what characteristics of consumers typically online shopping is and what their attitude in online shopping is. In simple terms, this means that there is no point having an excellent product online if the types of consumers who would buy it are unlikely to be online. 2 Demographic Factors On top of that, Bellman (1999) investigated various predictors for whether an individual will purchase online. These authors concluded that demographic variables such as income, education and age have a modest impact on the decision of whether to buy online whereas the most important determinant of online shopping was previous behavior such as earlier online purchases. 224

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This is consistent with Forrester Research which proved that demographic factors do not have such a high influence on technology as the consumers‟ attitudes do (Modahl, 2000). 3 Pattern of Online Buying According the study which was done by Master Card Worldwide Insights (2008), the product and services most frequently bought online among Asia/Pacific online shopper are books and art (41%), home appliances and electronic products (39%), CDs/DVDs/UCDs (38%) and ladies clothing/accessories (38%). Opportunistic buying as a whole does not seem to be a major factor for many online shoppers: 41% bought on impulse just a couple of times, while 34% hardly ever bought on impulse. Similar to the types of products frequently purchased online, items most likely to result in opportunistic buying were ladies clothing and accessories, home appliances and electronic products and CDs/DVDs/VCDs. In addition, consumers‟ previous experiences with online purchases or lack thereof can be a significant influence of levels of risk perception by consumers and their purchasing decisions (Dillon, 2004). Negative experiences increase levels of risk perception with online purchasing and hamper not only a business likelihood of retaining customers but can make it more difficult for other online businesses to gain initial customers (Boyer, 2005). According to Leggatt (2010), a quarter of U.S. adults have increased the amount of time they spend online shopping (24%) and reading product reviews (25%), found Harris Interactive 's online survey. Younger adults, aged 18-34, have increased their time spent doing both of these activities more than older adults, leading to speculation that this trend will continue. Americans are spending more time researching purchases and shopping online, according to Harris Poll findings, and many are feeling the social consequences of life in front of a monitor. 2.4 Purchase Perception It has been reported that consumers have a low perception and trust of online merchants, making them unwilling to make purchases online. The results of a survey of 9700 online consumers showed that three out of five respondents did not trust web merchants (Belanger, Hiller, & Smith, 2002) Apart from that, customer service affects purchase decisions through vendor knowledge, responsiveness and reliability (Baker, Levy, and Grewal, 1992; Gefen, 2002). Internet purchases of tangible goods present unique challenges when compared with traditional „brick and mortar‟ retail store purchases. Consumers do not have the opportunity to physically inspect goods purchased over the internet prior to purchasing them (Jarvenpaa and Todd, 1996-97). Instead, internet purchasers must rely on mediated representations of the goods being purchased, are normally dependent on third parties for delivery of purchased goods and may question the convenience of product returns. Customer service variables of vendor knowledge, responsiveness (delivery time and return convenience) and reliability are examined in this study. Lastly, the concept of risk is important for understanding how internet consumers make choices (Hasan and Rahim 2004). Shopping environments on the internet may be uncertain for the majority of online shoppers especially if they are novices. The risk may then be defined as the subjectively-determined expectation of loss by an online purchaser in contemplating a particular online purchase. Amongst the identified perceived risk are financial, product performance, social, psychological and time/ convenience loss. Financial risk stems from paying more for a product than being necessary or not getting enough value for the money spent (Roehl and Fesenmaier 1992).
METHODOLOGY

1. Study location: Respondents were selected from Taman Tawas Permai, Ipoh, Perak. This location is selected by the researcher because it is convenience for the researcher and the accessibility and coverage is broad enough. Researcher was survey the factors influencing consumers‟ attitude and perception to make e-commerce purchase through online shopping from range of age in this area. This is to avoid bias for surveying all the respondents from only a certain range of age only. 2. Sampling Method: This study was conducted by convenience sampling method because of the unavailability of the list online shopper that involved in online purchases. There were 100 respondents in this research study. Anonymity and confidentiality were assured and participants were told that they could withdraw from the study at any point without prejudice. 225

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The respondents were drawn from different occupational categories, education, age, gender or ethnic categories but all of them fulfilled the basic condition mentioned earlier. 3. Instruments The main instrument for this study was a questionnaire. The questionnaire aimed to gather information about respondents‟ socio-demographic background, attitude towards online shopping and purchase perception towards online shopping. Therefore, the questionnaire was used to assess knowledge of online purchasing. The questions were developed based on literature review which found to have high readability and good validity. The questionnaire was divided into three parts. 4. Pre-test Pre-test was done prior to the actual research. This pre-test is involved 10 respondents in order to ensure that the questions are understandable by the actual respondents. It was also aimed to determine the reliability alpha for each instruments used beside to achieve research precise research objectives. Moreover, pre-test allow researcher to improve the scarify that existed in questionnaire form and to make sure that the items was suit with the study‟s requirement. 5. Data Collection and Data Analyze A survey was conducted in the early of November 2010 and 100 questionnaires were returned by end of December 2010. Self-administered questionnaire was used for this study in order to obtain data. The questionnaire was conducted in English which is consisted of both open-ended and close-ended questions. The data were analyzed using the “Statistical Package for the Social Sciences” (SPSS for Windows version 13). The main statistical analysis was descriptive statistics such as frequency, percentage and mean were calculated to describe respondents‟ background and patterns of using internet and buying online. Besides, the level of score for attitude towards online purchasing and purchase perception towards online purchasing were categorized into three levels which is low, medium and high by using the highest score and the lowest score. One-way ANOVA was used to assess the differences between independent variable such as age, income, occupation and pattern of online buying (type of goods). Pearson‟s correlation was used to assess the relationship between independent variable such as ecommerce experience, hours spent on internet, product perception, customers‟ service and consumers‟ risk.
RESEARCH FINDING AND DISCUSSION

Research findings found that more than half of the respondents have medium level of attitude and purchase perception towards online shopping. There were 9 hypotheses in this study; five of them are rejected via the inferential statistical analysis. Meanwhile, the other hypotheses are fail to be rejected. 1. To investigate how socio-demographic (age, income and occupation) affect consumers’ attitude towards online shopping. H01: There is no significant difference in attitude towards online shopping among age group. One-way ANOVA was utilized to examine the differences in attitude towards online shopping among age group. The result of this analysis was summarized in Table 4.5.1. From the Table 5.1, the research finding showed that there was no significant difference in attitude towards online shopping among age group (F = 1.020, p < 0.05). Hence, H01 was fail to be rejected. This showed that the age of the respondents do not have effect on consumers‟ attitude to make e-commerce purchases through online shopping. H02: There is no significant difference in attitude towards online shopping among income group. One-way ANOVA was utilized to examine the differences in attitude towards online shopping among income group. The result of this analysis was summarized in Table 4.5.2. From the Table 4.5.2, the research finding showed that there was a significant difference in attitude towards online shopping among income group (F = 0.556, p > 0.05). Hence, H02 was successfully rejected. This showed that income have effect on consumers‟ attitude to make e-commerce purchases through online shopping. H03: There is no significant difference in attitude towards online shopping among occupation group. One-way ANOVA was utilized to examine the differences in attitude towards online shopping among occupation group. The result of this analysis was summarized in Table 4.5.3. 226

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From the Table 4.5.3, the research finding showed that there was no significant difference in attitude towards online shopping among occupation group (F = 1.607, p < 0.05). Hence, H 03 was fail to be rejected. This showed that the occupation of the respondents do not have effect on consumers‟ attitude to make e-commerce purchases through online shopping. 2. To probe how the pattern of online buying (types of goods, e-commerce experience and hours use on internet) influence consumers’ attitude towards online shopping. H04: There is no significant difference in attitude towards online shopping among types of goods group. One-way ANOVA was utilized to examine the differences in attitude towards online shopping among types of goods group. The result of this analysis was summarized in Table 4.5.4. From the Table 4.5.4, the research finding showed that there was no significant difference in attitude towards online shopping among types of goods group (F = 1.384, p < 0.05). Hence, H04 was fail to be rejected. This showed that the pattern of online buying (types of goods) of the respondents do not have effect on consumers‟ attitude to make e-commerce purchases through online shopping. H05: There is no significant relationship between e-commerce experience and attitude towards online shopping. Pearson Correlation test was utilized to examine the relationship between the e-commerce experience and attitude towards online shopping. The result of this analysis was summarized in Table 4.5.5. Table 4.5.5 shows that there was significant relationship between e-commerce experience and attitude towards online shopping among the respondents (r = -0.236**, p < 0.05). Hence, H05 was successfully rejected. This showed that e-commerce experience have effect on consumers‟ attitude to make e-commerce purchases through online shopping. H06: There is no significant relationship between hours spent on internet and attitude towards online shopping. Pearson Correlation test was utilized to examine the relationship between hours spent on internet and attitude towards online shopping. The result of this analysis was summarized in Table 4.5.6. Table 4.5.6 shows that there was no significant relationship between hours spent on internet and attitude towards online shopping among the respondents (r = 0.106, p > 0.05). Hence, H06 was fail to be rejected. This showed that the respondents‟ averaged hours spent on internet do not have effect on consumers‟ attitude to make e-commerce purchases through online shopping. 3. To examine how purchase perception (product perception, customer service and consumer risk) influence consumers’ attitude towards online shopping. H07: There is no significant relationship between product perception and attitude towards online shopping. Pearson Correlation test was utilized to examine the relationship between product perception and attitude towards online shopping. The result of this analysis was summarized in Table 4.5.7. Table 4.5.7 shows that there was significant relationship between e-commerce experience and attitude towards online shopping among the respondents (r = 0.471**, p < 0.01). Hence, H07 was successfully rejected. This showed that product perception have effect on consumers‟ attitude to make e-commerce purchases through online shopping. H08: There is no significant relationship between customers’ service and attitude towards online shopping. Pearson Correlation test was utilized to examine the relationship between customers‟ service and attitude towards online shopping. The result of this analysis was summarized in Table 4.5.8. Table 4.5.8 shows that there was significant relationship between customers‟ service and attitude towards online shopping among the respondents (r = 0.459**, p < 0.01). Hence, H08 was successfully rejected. This showed that customers‟ service have effect on consumers‟ attitude to make e-commerce purchases through online shopping. H09: There is no significant relationship between consumers’ risk and attitude towards online shopping. Pearson Correlation test was utilized to examine the relationship between consumers‟ risk and attitude towards online shopping. The result of this analysis was summarized in Table 4.5.9. Table 4.5.9 shows that there was no significant relationship between consumers‟ risk and attitude towards online shopping among the respondents (r = 0.153, p > 0.05). Hence, H09 was fail to be rejected. This showed that consumers‟ risk do not have effect on consumers‟ attitude to make e-commerce purchases through online shopping. 227

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Table 4.6: Summary of Statistical Analysis of Hypothesis Specific Objective To investigate how socio-demographic (age, income and occupation) affect consumers‟ attitude towards online shopping. Hypotheses Statistical Test Ho1: There is no significant difference One-way between age and attitude towards online ANOVA shopping. Ho2: There is no significant difference One-way between income and attitude towards ANOVA online shopping. Ho3: There is no significant difference One-way between occupation and attitude towards ANOVA online shopping. Ho4: There is no significant difference One-way between pattern of online buying (types ANOVA of goods) and attitude towards online shopping. Ho5: There is no significant relationship Pearson between e-commerce experience and Correlation attitude towards online shopping. Test Ho6: There is no significant relationship Pearson between hours spent on internet and Correlation attitude towards online shopping. Test Ho7: There is no significant relationship Pearson between product perception and attitude Correlation towards online shopping. Test Ho8: There is no significant relationship Pearson between customer service and attitude Correlation towards online shopping. Test Ho9: There is no significant relationship Pearson between consumer‟ risk and attitude Correlation towards online shopping. Test Result F = 1.020 p < 0.05 F = 0.556 p > 0.05 F = 1.607 p < 0.05 F = 1.384 p < 0.05 Discussion Fail to reject

Rejected

Fail to reject

To probe how the pattern of online buying (types of goods, e-commerce experience and hours use on internet) influence consumers‟ attitude towards online shopping. To examine how purchase perception (product perception, customer service and consumer risk) influence consumers‟ attitude towards online shopping.

Fail to reject

r = - Rejected 0.236** p < 0.05 r = 0.106 Fail to reject p > 0.05 r = 0.471** p < 0.01 r = 0.459** p < 0.01 r = 0.153 p > 0.05 Rejected

Rejected

Fail to reject

CONCLUSION

Eventually, from the nine hypotheses that have been formed, only four hypotheses were rejected via the statistical analysis. The first specific objective is to investigate how demographic (age, occupation and income) affect consumers‟ attitude towards online shopping. From the research, it found that there was no significant difference in attitude towards online shopping among age group (F = 1.020, p < 0.05). Hence, it was fail to be rejected. However, there was a significant difference in attitude towards online shopping among income group (F = 0.556, p > 0.05). Hence, it was successfully rejected. The result also indicated that there was no significant difference in attitude towards online shopping among occupation group (F = 1.607, p < 0.05). Hence, it was fail to be rejected. The second specific objective is to probe how the pattern of buying online (types of goods, e-commerce experience and hours use on internet) influence consumers‟ attitude towards online shopping. The research finding showed that there was no significant difference in attitude towards online shopping among types of goods group (F = 1.384, p < 0.05). Hence, it was fail to be rejected. However, there was significant relationship between e-commerce experience and attitude towards online shopping among the respondents (r = -0.236**, p < 0.05). Hence, it was successfully rejected. Also, there was no significant relationship between hours spent on internet and attitude towards online shopping among the respondents (r = 0.106, p > 0.05). Hence, it was fail to be rejected. The third specific objective is to examine how purchase perception (product perception, customer service and consumer risk) influence consumers‟ attitude towards online shopping. The result findings show that there was significant relationship between e-commerce experience and attitude towards online shopping among the respondents (r = 0.471**, p < 0.01). 228

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Hence, it was successfully rejected. Same result obtained for relationship between customer service and attitude towards online shopping, there was significant relationship between customer service and attitude towards online shopping among the respondents (r = 0.459**, p < 0.01). Hence, it was successfully rejected. Yet, the result shows that there was no significant relationship between risk and attitude towards online shopping among the respondents (r = 0.153, p > 0.05). Hence, it was fail to be rejected.
IMPLICATION

There are few reasons why investigating on factors that influencing consumers‟ attitude towards online shopping is important. From the marketer‟s perspective, they will more understand the attitude of the consumers towards online shopping as well as the factors influencing consumers to make e-commerce purchases. From the result, they can know that e-commerce experience, product perception and customer service have significant relationship with attitude towards e-commerce purchases through online shopping. On top of that, they can also know that the consumers who purchase online are more likely to buy clothes, book and make travel booking. From the consumer‟s perspective, they will know that there are many advantages of online shopping such as it will be more convenience shopping on the internet and there is no crowd of people when shopping through online. This research can make the consumers aware that e-commerce is becoming an important trend in this modern information technology society. Last but not least, this study is useful for the academicians where current study could serve as a reference and may provide some guides for the future researchers who would like to study about the same topic.
RECOMMENDATION

This study has taken important steps to investigate the attitude towards online shopping and the factors that influencing consumers‟ attitude to make e-commerce purchases. Despite this study has strengths, it has certain limitations. Firstly, the research has only examines three factors that influencing consumers‟ attitude towards online shopping. Future researches are suggested to determine other factors that influencing consumers‟ attitude towards online shopping beside consumers‟ socio-demographic, pattern of buying online and purchase perception. Therefore, it helps them to understand other factors that may influence the consumers‟ attitude towards online shopping. Besides, future researchers may further scope to replicate the study in different environment and different geographical locations. Different environment played a vital factor that affect respondents‟ attitude towards online shopping. Individuals in the busy environment like capital city could behave in a different manner compared with this sample. This study was conducted at Taman Tawas Permai, Ipoh. Therefore, it could not represent other people in big city where the quality of life and approaches are different.

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References
Baker, J., Levy, M., & Grewal, D. (1992). An experimental approach to making retail store environment decisions. Journal of Retailing, 64(4), 445-460. Belanger, F., Hiller, J.S., & Smith, W.J. (2002). Trustworthiness in Electronic Commerce: the role of privacy, security, and site attributes. Journal of Strategic Information Systems, 11, 245-270. Bellman, S., Lohse, G. and Johnson, E. (1999).„Predictors of online buying behaviour,‟ Communications of the ACM, 42 (12), 32-38. Bem, D. 1980. The concept of risk in the study of human behavior. In Risk and Chance: Selected Readings. J. Dowie and P.L. Milton, II, eds. Keynes, England: The Open University Press. 1 – 15 Boyer, K. K., & Hult, G. T. M. (2005). Customer behavior in an online ordering application: A decision scoring model. Journal of Decision Sciences, 36(4), 569-598. Chiu, Y. B., Lin, C. P., and Tang, L. L. (2005). Gender differs: assessing a model of online purchase intentions in e-tail service. International Journal of Service Industry Management. 16(5). Dillon, T. D., and Reif, H. L. (2004). Factors influencing consumer‟s e-commerce commodity purchases. Information Technology, Journal of Learning and Performance, 22(2), 1-12. Hasan, H., and Rahim, S. A. (2004). Factors affecting online purchasing behavior. Journal of Communication, 24, 1-19. Jarvenpaa, S. L., & Todd, P. A. (1996-97). Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, 1(2), 59-88. Leggatt, H. (2010). Time spent shopping online increases, face-to-face time decreases. Retrieved April 29, 2011, from http://www.bizreport.com/2010/11/time-spent-shopping-online-increases-face-to-face-time-decreases.html Master Card Worldwide Insights, (2008). Online shopping in Asia/Pacific-Patterns, trends and future growth. Retrieved April 29, 2011, from http://www.mastercard.com/us/company/en/insights/pdfs/2008/Asia_Pacific_Online_Shop.pdf Modahl, M. (2000). Now or Never: How Companies Must Change to Win the Battle for the Internet Consumer, Harper Business: New York. Mohd Suki, N., Mohd Ismail, A., and Thyagarajan, V. (2006). A study on the effect of normative belief on online shopping innovativeness: structural equation modeling analysis Paper presented at the The Brunei, Indonesia, Malaysia and Philippines – East Asean Growth Area (BIMP-EAGA) Conference 2006, June 21-23. Parker-Hall, J. (2009). The history of online shopping. Retrieved September 1, 2010, from http://ezinearticles.com/?The-History-of-Online-Shopping&id=2592183 Roehl, W.S. and Fesenmaier, D.R. 1992. Risk Perceptions and pleasure travel: An exporatory analysis. Journal of Travel Research, 30(4): 17–26. Schlosser, A.E. (2003a). Experiencing products in the virtual world: the role of goal and imagery in influencing attitudes. Journal of Consumer Research, Vol. 30, pp. 184-198. Wikipedia, (2010). Online shopping. Retrieved September 1, 2010, from http://en.wikipedia.org/wiki/Online_shopping

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References: Baker, J., Levy, M., & Grewal, D. (1992). An experimental approach to making retail store environment decisions. Journal of Retailing, 64(4), 445-460. Belanger, F., Hiller, J.S., & Smith, W.J. (2002). Trustworthiness in Electronic Commerce: the role of privacy, security, and site attributes. Journal of Strategic Information Systems, 11, 245-270. Bellman, S., Lohse, G. and Johnson, E. (1999).„Predictors of online buying behaviour,‟ Communications of the ACM, 42 (12), 32-38. Bem, D. 1980. The concept of risk in the study of human behavior. In Risk and Chance: Selected Readings. J. Dowie and P.L. Milton, II, eds. Keynes, England: The Open University Press. 1 – 15 Boyer, K. K., & Hult, G. T. M. (2005). Customer behavior in an online ordering application: A decision scoring model. Journal of Decision Sciences, 36(4), 569-598. Chiu, Y. B., Lin, C. P., and Tang, L. L. (2005). Gender differs: assessing a model of online purchase intentions in e-tail service. International Journal of Service Industry Management. 16(5). Dillon, T. D., and Reif, H. L. (2004). Factors influencing consumer‟s e-commerce commodity purchases. Information Technology, Journal of Learning and Performance, 22(2), 1-12. Hasan, H., and Rahim, S. A. (2004). Factors affecting online purchasing behavior. Journal of Communication, 24, 1-19. Jarvenpaa, S. L., & Todd, P. A. (1996-97). Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, 1(2), 59-88. Leggatt, H. (2010). Time spent shopping online increases, face-to-face time decreases. Retrieved April 29, 2011, from http://www.bizreport.com/2010/11/time-spent-shopping-online-increases-face-to-face-time-decreases.html Master Card Worldwide Insights, (2008). Online shopping in Asia/Pacific-Patterns, trends and future growth. Retrieved April 29, 2011, from http://www.mastercard.com/us/company/en/insights/pdfs/2008/Asia_Pacific_Online_Shop.pdf Modahl, M. (2000). Now or Never: How Companies Must Change to Win the Battle for the Internet Consumer, Harper Business: New York. Mohd Suki, N., Mohd Ismail, A., and Thyagarajan, V. (2006). A study on the effect of normative belief on online shopping innovativeness: structural equation modeling analysis Paper presented at the The Brunei, Indonesia, Malaysia and Philippines – East Asean Growth Area (BIMP-EAGA) Conference 2006, June 21-23. Parker-Hall, J. (2009). The history of online shopping. Retrieved September 1, 2010, from http://ezinearticles.com/?The-History-of-Online-Shopping&id=2592183 Roehl, W.S. and Fesenmaier, D.R. 1992. Risk Perceptions and pleasure travel: An exporatory analysis. Journal of Travel Research, 30(4): 17–26. Schlosser, A.E. (2003a). Experiencing products in the virtual world: the role of goal and imagery in influencing attitudes. Journal of Consumer Research, Vol. 30, pp. 184-198. Wikipedia, (2010). Online shopping. Retrieved September 1, 2010, from http://en.wikipedia.org/wiki/Online_shopping 230