Perceived risks as barriers to Internet and e-commerce usage

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1 Perceived risks as barriers to Internet and e-commerce usage Yehoshua Liebermann and Shmuel Stashevsky The authors Yehoshua Liebermann is Executive MBA Director and Shmuel Stashevsky is International MBA Director, both at the Graduate School of Business Administration, Bar-Ilan University, Ramat Gan, Israel. Keywords Risk, Perception, Consumer behaviour, Internet, Business development, Marketing Abstract Previous research suggests that perceived risk is an important ingredient in the consumer decision-making process. The purpose of the present study is to investigate what are the perceived barriers to Internet usage and e-marketing by both users and non-users. By understanding these potential obstacles, more efficient marketing strategies will become available that will drive Internet use and e-commerce. A detailed perceived risks map has been developed using a qualitative research paradigm. We suggest a model with the factors affecting the Internet s perceived risk elements. The factors are demographic traits and usage behavior characteristics. The model is tested against a sample of 465 employed adults. Electronic access The research register for this journal is available at The current issue and full text archive of this journal is available at Volume 5. Number pp. 291±300 # MCB UP Limited. ISSN DOI / Introduction Internet usage is growing rapidly all over the globe. As reported by Nua, Internet users over the world amounted to 513 million in August 2001, 8.5 per cent of the world s population (nua.com). Global usage growth rate for the previous 12 months is estimated at 40 per cent. In the USA and Canada 166 million were online in August 2001, 60 per cent of the population (NielsenNetRatings, 2001a). In Israel, 39 per cent of the population above 16 has home Internet access, 18 per cent have work access, and 15 per cent have access at other places (NielsenNetRatings, 2001b). The recent slowdown in Internet business does not necessarily have an immediate effect on usage trends and rates. Although these figures seem quite impressive, especially in view of the short and bumpy history of the Internet, they by no means indicate usage peaks or saturation. On the contrary, sellers of Internet time have much left to do. Marketing efforts intended to enhance Internet use are expected to follow a twofold strategy: turn non-users into new users and expand usage of current users. Theoretically, both strategic avenues seem plausible. Nonetheless, the job is not an easy one to perform. As is the case with more traditional products, no success prescription can be guaranteed. Consumers make their own decisions as to how much to consume and/or use of an abundant array of products and services. Decisions of this type are made for each good by weighing various utilities derivable from using an item against what it takes from the consumer in terms of different resources. In a more theoretical phrasing a traditional economic model would claim that consumers make their consumption/usage decisions by equating marginal utility (or benefit) to marginal cost associated with each given item considered (Katona, 1953). There is no a-priori reason to distinguish between Internet as a consumption good to any other economic service or product. Internet can be characterized in terms of both utility and cost to users (Morris and Turner, 2001; Thibodeaux, 2000). Conceptually, consumers attitudes towards Internet usage can be described along the lines of marginal utility and cost considerations that determine an equilibrium amount of usage. Such a model can be developed even to explain and/ 291

2 or predict a diffusion path of a new service like Internet along time. As Liebermann and Paroush (1982) show adoption rates of newly offered goods depend crucially on the marketer s ability to mitigate perceived risk involved with new goods offered. Clearly, the effect of perceived risk is not limited to the deliberation of non-users who explore the option of becoming users. It is equally relevant to users who consider expanding use. To illustrate, a cell-phone user may be deterred from increasing usage due to perceived risk of extra radiation. The purpose of the present study is to investigate the role of perceived risks as potential barriers to Internet and e-commerce usage. In order to fulfill this goal a research design of three stages has been applied. First, using a qualitative research approach a detailed map of perceived risks has been produced. Second, a model describing factors affecting perceived risks has been constructed, using a qualitative scheme of analyzing inter-relationships between given variables. Finally, the model has been tested empirically against field data. Conceptual framework In consumer behavior literature perceived risk is a long rooted central concept (Cox and Rich, 1964). Its centrality is due to its multidimensional nature. Traditionally, it is common to decompose overall perceived risk to different species as financial, physical, psychological (or mental), and social (Bettman, 1973). Following Dekimpe et al. (2000), it seems that for Internet and other hi-tech items product group specific technological risk (fear of technologically complicated innovations) can be added to the list. This set of perceived risk components implies that in order to expand Internet usage, suppliers must first learn what are the potential obstacles faced by potential users and non-users and only then attempt to design creative marketing solutions. Those solutions will enable potential customers to handle perceived risk more effectively and ultimately reduce it to acceptable levels. Such an approach amounts to creating an efficient user-friendly decision support system, as conceptualized by Silverman et al. (2001). Consumers supported by a system of this type will be more likely to either turn from 292 non-shoppers into active shoppers, or increase their previous shopping volume. It is the purpose of the present study to investigate what are the perceived barriers to Internet usage and e-marketing by both users and non-users. By understanding these potential obstacles more efficient marketing strategies will become available that will drive Internet use and e-commerce. Previous research suggests that perceived risk is an important ingredient in consumer Internet decision-making process. For example, Donthu and Garcia (1999) found that Internet shoppers are less risk averse than Internet non-shoppers are. This distinction indicates that non-users are likely to perceive higher levels of subjective risk associated with Internet usage as compared with users. Nevertheless, Donthu and Garcia (1999) do not spell out specific risk components as perceived by consumers. Practically, an accurate identification of different perceived risk types is essential for Internet service providers. Understanding the barriers that inhibit use of potential buyers enables providers to employ suitable means that are intended to help consumers reduce risk levels (Jarvenpaa et al., 2000). In turn, lowered perceived risk levels are expected to enhance consumers responses in terms of purchase intention and actual sales (Mitchell et al., 1999). A preliminary recent literature review reveals a series of main perceived risk components, notably privacy and security. Privacy attracts considerable attention as increasing amounts of information flow through various electronic communication channels (Introna and Pouloudi, 1999). Indeed, business organizations implement various measures to protect present and future Internet shoppers (Salkin, 1999), but electronic shopping channels provoke considerable perceived risk to many consumers. A more detailed study (Jones and Carlson, 2001) focused on privacy concerns of users regarding different items of personal information. To illustrate, their findings indicate that 73 per cent felt not comfortable at all giving up their social security number. In general, they found that the majority of users express concern about Internet privacy, although only 28 per cent are very concerned. Another recent survey by Pew Internet and American Life Project (2000)

3 estimated Internet user fears. The two main concerns were: (1) Business people you do not know getting personal information about you and your family (84 per cent of the users were concerned). (2) Computer hackers getting your credit card number online (68 per cent were concerned). Privacy is not the only source of subjective Internet risk as perceived by consumers. Miyazaki and Fernandez (2001) maintain that privacy and security risk perceptions are the major obstacles in the development of consumer related e-commerce activities. Some of the sources cited above document security concerns as well. The Gallup poll analysis by Jones and Carlson (2001) reports that 46 per cent of users are very concerned by misuse of credit card information given out on the Internet, while 36 per cent are somewhat concerned. Altogether, 82 per cent are concerned to some degree about security issues. Apart from privacy and security, both users and non-users generate perceived risks regarding other risk components. Miyazaki and Fernandez (2001) mention that perceived risks derive from the relative novelty of the Internet. However, we view novelty itself as an independent risk component. Public surveys as Nielsen and Gallup address some other risk types. In their analysis of the Gallup poll scores regarding users, Jones and Carlson (2001) direct attention to such risks as being tracked by Internet cookies, monitoring of use by Internet provider or employer, and receiving undesired s. The risk of unwanted junk mail is also identified by Pew Research Center (1998). Two additional perceived risk components are dealt with on a much more limited scale in the literature. Armstrong et al. (2000) refer to Internet addiction as potential perceived risk. High fees are a source of perceived financial risk to Israeli users (Pew Research Center, 1998). Concern about slow connection and responses can be interpreted as some sort of technological risk. As shown by Pew Research Center (1998), consumers worry about slow operation as well. This finding has been identified by other researchers such as Hoag (1998) and Sevcik and Bartlett (2001). Interestingly enough, in terms of physical perceived risk, a recent study conducted by 293 Vividence Corporation (2001) before and after September 11, 2001 shows that 29 per cent of the respondents claim that they would shop more online since they are concerned about safety in stores. In the present study a detailed perceived risks map is researched. Qualitative research, based on reviewing scientific literature, interviews with Internet experts, and interviews with consumers, identified the following nine different risk components: (1) Internet credit card stealing; (2) supplying personal information; (3) pornography and violence; (4) vast Internet advertising; (5) information reliability; (6) lack of physical contact; (7) not supplying Internet products purchased; (8) missing the human side in Internet purchases; and (9) Internet usage addiction. As done in some sources cited above, a distinction is made between users and nonusers. Demographic effects as documented in the literature pertain to differences in Internet usage. The main effects considered are those of gender and age. In the present research the differences based on gender, age, marital status and education are applied to potential risk perception. Following Miyazaki and Fernandez (2001) the effect of usage activity volume on the perceived risk elements is analyzed as well. The model we suggest to analyze the variables that affect perceived risk elements is presented in Figure 1. Hypotheses H1. The two main perceived risk elements are Internet credit card stealing and supplying personal information. Figure 1 The factors affecting the Internet perceived risk elements: demographic traits

4 H2. Internet users are likely to perceive higher risks in the Internet than non-users concerning technical elements. H3. Females are likely to perceive higher risks in the Internet than males. H4. Older people are likely to perceive higher risks in the Internet than younger ones. H5. Married people are likely to perceive higher risks in the Internet than unmarried people. H6. People with low education level are likely to perceive higher risks in the Internet than high-educated people. H7. Internet users that never bought online are likely to perceive higher risks in the Internet than Internet users that actually bought online. H8. Light Internet users are likely to perceive higher risks in the Internet than heavy Internet users. Methodology Subjects The respondents were 465 employed adult Israelis from a variety of organizations. Response rate was about 85 per cent. A total of 372 Internet users (80 per cent) were distinguished from 93 non-users (20 per cent). Sample characteristics The sample characteristics are: Of the sample, 58 per cent were male and 42 per cent female. The majority of the sample (68 per cent) was married, 25 per cent single, 6 per cent divorced and 1 per cent widowed. age is 37. Education: elementary school 1 per cent, high school 22 per cent, partial higher education 17 per cent, BA degree 36 per cent, MA or PhD degree 24 per cent. The majority of the sample (87 per cent) has a computer at home. The majority of the sample (80 per cent) consists of Internet users. Out of the Internet users 80 per cent use the Internet at home, 79 per cent use the Internet at work, and 13 per cent use the Internet at an educational institute. Measures Respondents were interviewed by means of a written questionnaire researching their Internet and e-marketing usage habits and perceptions. 294 The questionnaire was composed of 76 questions covering different aspects of Internet usage. Users were asked first to document their usage behavior (e.g. hours per week, number of messages sent per week) then their level of satisfaction of various service attributes (e.g. access time, accessibility) and finally their various usage channels (e.g. news, search engines). Non-users were asked as to the reasons for their refraining from use (e.g. access to computers, cost of service). Both users and non-users were asked jointly two sets of questions regarding their general attitudes to Internet (e.g. privacy protection, addiction) and their socio-demographic traits. Except for the usage behavior and demographic parts all answers were closed form measured on a six-point Likert scale. The main variables that were analyzed in this study are the following nine perceived risk elements: (1) Internet credit card stealing; (2) supplying personal information; (3) pornography and violence; (4) vast Internet advertising; (5) information reliability; (6) lack of physical contact; (7) not supplying Internet products purchased; (8) missing the human side in Internet purchases; and (9) Internet usage addiction. The answer scale for the perceived risk elements was: 1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = slightly agree, 5 = agree, 6 = strongly agree. The four demographic traits that were analyzed in this study and their scales are: (1) Gender: 1 = male, 2 = female. (2) Younger/older age: 1 = age up to 35, 2 = age above 35. (3) Married/unmarried: 1 = married, 2 = unmarried. (4) High/low education: 1 = high education (at least BA degree), 2 = low education. The three usage behavior variables that were analyzed in this study and their scales are: (1) Internet user/non-user: 1 = Internet user, 2 = Internet non-user. (2) Bought online: 1 = Internet user who bought online at least once, 2 = Internet user who never bought online. (3) Heavy/light Internet user: 1 = heavy user (above mean Internet usage hours), 2 = light user.

5 Data analysis T-test for analyzing mean differences of independent samples was applied to analyze independently the mean of the perceived risk elements of each one of the sub-populations included in this study. Socio-demographic traits and usage behavior characteristics are tested independently since consumer information in typical Internet Web sites is rarely complete. Consequently, Internet service providers may need to make operational decisions based on partial information only. Descriptive statistics and correlation coefficients were also presented in order to enable additional insights. The statistical package used in this study was SPSS. Results 1. Descriptive statistics and relationship among variables Table I presents the mean, standard deviation of the perceived risk variables and Figure 2 shows their ranking in a bar chart. Table II presents the correlation coefficients among them. The two main perceived risk elements are Internet credit card stealing and supplying personal information. Therefore, H1 was supported. 2. Differences between Internet users and non-users Table III and Figure 3 show the average rankings of the perceived risk elements for the two segments: Internet users and non-users. From Table III it can be seen that the two most significant risks are: Internet credit card stealing and supplying personal information. Significant user/non-user differences are measured for two risks: Internet credit card stealing and vast Internet advertising. Users perceived risk of these two elements is significantly higher as compared to non-users. Therefore, H2 that Internet users will perceive higher risks in the Internet than nonusers concerning the technical elements was partly supported. Table I and standard deviation of the perceived risk elements (all samples) n = 465 Standard deviation 1. Internet credit card stealing Supplying personal information Pornography and violence Vast Internet advertising Information reliability Lack of physical contact Not supplying Internet products purchased Missing the human side in Internet purchases Internet usage addiction Figure 2 s ± all samples 295

6 Table II Correlation coefficients among the perceived risk variables (Pearson coefficients) Study variables Credit card stealing 1 2. Supplying personal information 0.56* 1 3. Pornography and violence 0.38* 0.41* 1 4. Vast Internet advertising 0.17* 0.26* 0.23* 1 5. Information reliability 0.29* 0.37* 0.46* 0.35* 1 6. Lack of physical contact 0.32* 0.32* 0.25* 0.15* 0.24* 1 7. Not supplying products 0.40* 0.38* 0.34* 0.26* 0.35* 0.53* 1 8. Missing the human side 0.29* 0.27* 0.27* 0.20* 0.26* 0.76* 0.46* 1 9. Internet usage addiction 0.17* 0.22* 0.40* 0.29* 0.34* 0.26* 0.31* 0.30* 1 Notes: * Correlation is significant at the 0.05 level (two-tailed), n = 465 Table III s, mean rankings Users (80 per cent) Non-users (20 per cent) 1. Internet credit card stealing 4.61* 4.17* 2. Supplying personal information Pornography and violence Vast Internet advertising 3.76* 3.04* 5. Information reliability Lack of physical contact Not supplying Internet products purchased Missing the human side in Internet purchases Internet usage addiction Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 465 Figure 3 s: users and non-users 3. Effects of demographic traits Naturally, it is intriguing to understand why given risk elements are ranked differently by respondents. One potential source affecting consumer decisions and attitudes is their individual demographic traits. Tables IV-VII list the mean of the perceived risk elements associated with each sub-population according to the following demographic traits: gender; age; marital status; and education. T-test analysis of independent samples was applied to test if the mean difference was statistically significant. 296

7 Table IV Effect of gender, mean rankings Male (58 per cent) Female (42 per cent) 1. Internet credit card stealing 4.40* 4.68* 2. Supplying personal information Pornography and violence 3.57* 4.08* 4. Vast Internet advertising 3.76* 3.44* 5. Information reliability Lack of physical contact Not supplying Internet products purchased Missing the human side in Internet purchases 2.98* 3.34* 9. Internet usage addiction Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 465 Table V Effect of age, mean rankings Up to 35 (54 per cent) Age Above 35 (46 per cent) 1. Internet credit card stealing Supplying personal information 4.26* 4.51* 3. Pornography and violence 3.58* 4.03* 4. Vast Internet advertising Information reliability Lack of physical contact Not supplying Internet products purchased Missing the human side in Internet purchases 2.99* 3.28* 9. Internet usage addiction 2.75* 3.15* Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 465 Table VI Effect of marital status, mean rankings Married (68 per cent) Unmarried (32 per cent) 1. Internet credit card stealing Supplying personal information 4.53* 4.08* 3. Pornography and violence 4.05* 3.24* 4. Vast Internet advertising Information reliability 3.72* 3.12* 6. Lack of physical contact Not supplying Internet products purchased Missing the human side in Internet purchases 3.23* 2.92* 9. Internet usage addiction 3.05* 2.66* Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 465 Females perceive significantly higher risks compared to males in three elements. Only the vast Internet advertising element worries men more than females. Therefore, H3 that females will perceive higher risks in the Internet than males was partially supported. The surprising finding is the lower perceived risk of females concerning the vast Internet advertising. Older people perceive significantly higher risks compared to younger people in four elements. Therefore, H4 that older people will perceive higher risks in the Internet than younger ones was partly supported. 297

8 Table VII Effect of education, mean rankings Academic education (60 per cent) Non-academic education (40 per cent) 1. Internet credit card stealing Supplying personal information Pornography and violence Vast Internet advertising Information reliability Lack of physical contact 3.24* 3.54* 7. Not supplying Internet products purchased Missing the human side in Internet purchases 3.00* 3.32* 9. Internet usage addiction 2.79* 3.09* Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 465 Married people perceive significantly higher risks in the Internet in the elements listed above compared to unmarried people. Therefore, H5 that married people will perceive higher risks in the Internet than unmarried people was partly supported. Those with non-academic education perceive significantly higher risks in the Internet in the asterisked elements than academically educated people. So H6 that people with low education level will perceive higher risks in the Internet than more educated people was partly supported. 4. Effects of usage behavior A complementary set of effects can be attributed to usage behavior. Tables VIII and IX exhibit a set of significant effects of this type. Internet users that never buy on the Internet perceive significantly higher risks in the Internet in the six elements listed compared to those who bought on the Internet. Therefore, H7 that Internet users that never bought online will perceive higher risks in the Internet than Internet users that actually bought online was partly supported. Internet light users perceive significantly higher risks in the Internet in the three elements listed above compared to those who are heavy Internet users. Therefore, H8 that light Internet users will perceive higher risks in the Internet than heavy Internet users was partly supported. Discussion and implications Perceived risks are key elements in the consumer behavior literature. Some studies focus on the perceived risks of the Internet and e-commerce (Donthu and Garcia, 1999; Introna and Pouloudi, 1999; Mitchell et al., 1999; Jarvenpaa et al., 2000; Miyazaki and Fernandez, 2001; Silverman et al., 2001). However, these studies consider a limited scope of perceived risk elements, especially security and privacy. In addition, they do not suggest a model to understand the influencing Table VIII Effect of actual Internet buying, mean rankings Internet user and buyer Internet user non-buyer (47 per cent) (53 per cent) 1. Internet credit card stealing 4.38* 4.79* 2. Supplying personal information 4.18* 4.60* 3. Pornography and violence 3.61* 3.95* 4. Vast Internet advertising Information reliability Lack of physical contact 2.97* 3.66* 7. Not supplying Internet products purchased 3.05* 3.63* 8. Missing the human side in Internet purchases 2.79* 3.36* 9. Internet usage addiction Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 372 Internet users 298

9 Table IX Effect of usage volume, mean rankings Heavy user over mean (41 per cent) Light user (59 per cent) 1. Internet credit card stealing Supplying personal information 4.18* 4.58* 3. Pornography and violence Vast Internet advertising Information reliability Lack of physical contact 2.99* 3.58* 7. Not supplying Internet products purchased Missing the human side in Internet purchases 2.87* 3.35* 9. Internet usage addiction Notes: * Statistically significant difference (at p < 0.05 two-tailed), n = 372 Internet users factors. In this study a detailed nine elements perceived risks map is researched. Furthermore, a model is suggested with the factors affecting the perceived risk elements. The model includes demographic traits and user behavior characteristics.... Another... finding identifies two central perceived risks that have a crucial effect on both Internet current and future users, and amount of usage: Internet credit card stealing and supplying personal information... The model and the eight proposed hypotheses were tested empirically with a sample of 465 employed adults. The hypotheses were supported regarding some of the perceived risk elements. Table X summarizes the study findings in terms of demographic and usage behavior effects. Another important finding identifies two central perceived risks that have a crucial effect on both Internet current and future users, and amount of usage: Internet credit card stealing and supplying personal information. These two perceived risk elements were obtained also as the major concerns of US Internet users in the Pew Internet and American Life Project (2001), but in reverse ranking. These findings can serve as preliminary guidelines to Internet marketers as well as Internet solution providers who would like to mitigate these effects in order to enhance Internet usage motivation and e-commerce activity of private consumers. In addition, it has been shown that both demographic and usage behavior traits have their own effects in terms of perceived risk formation. These multi-attribute findings can be used by marketers along the personalization process. Thereby each individual can be approached more efficiently according to his or her expected specific perceived risk structure. This way, personalized advertising messages and promotional offers can be adopted to reduce perceived risk by Internet users. Caution in interpreting the results is required. Generalizing from the responses of a Table X Significant differences in perceived risk elements by demographic traits and usage behavior characteristics Gender Age Married Education Internet user Buyer Usage volume 1. Internet credit card stealing * * * 2. Supplying personal information * * * * 3. Pornography and violence * * * * 4. Vast Internet advertising * * 5. Information reliability * 6. Lack of physical contact * * * 7. Not supplying products purchased * 8. Missing the human side in purchases * * * * * * 9. Internet usage addiction * * * Notes: * Statistically significant difference (at p < 0.05 two-tailed) 299

10 sample in a specific society to the behavior of consumers in other countries is not so simple. It could be argued, for example, that the sympathetic view of the respondents to the Internet is a result of their specific cultural, organizational, and/or personal attributes. Thus, studies of a similar nature with different types of cultural settings are recommended before trying to generalize these findings. The model of factors influencing perceived risk elements as advocated here may have implications for both future research and practice. More research is needed to identify additional influencing factors, such as personality traits. Another aspect that is recommended for future research is investigating, in a longitudinal study, the effects of perceived risk elements on actual buying behavior of the online consumers in the time to come. References Armstrong, L., Phillips, J.G. and Saling, L.L. (2000), ``Potential determinants of heavier Internet usage, International Journal of Human Computer Studies, Vol. 53, pp Bettman, J. (1973), ``Perceived risk and its components ± a model and empirical test, Journal of Marketing Research, Vol. 10, pp Cox, D.F. and Rich, S.U. (1964), ``Perceived risk and consumer decision making ± the case of telephone shopping, Journal of Marketing Research, Vol. 1, pp Dekimpe, M.G., Parker, P.M. and Sarvary, M. (2000), ``Global diffusion of technological innovations ± a coupled-hazard approach, Journal of Marketing Research, Vol. 37, pp Donthu, N. and Garcia, A. (1999), ``The Internet shopper, Journal of Advertising Research, Vol. 39 No. 3, pp Hoag, A. (1998), ``Measuring usage and satisfaction, D-Lib Magazine, March, available at: Introna, L.D. and Pouloudi, A. (1999), ``Privacy in the information age: stakeholders, interests and values, Journal of Business Ethics, Vol. 22 No. 1, pp Jarvenpaa, S.L., Tractinsky, N. and Vitale, (2000), ``Consumer trust in Internet store, Information Technology and Management, Vol. 1, pp Jones, J.M. and Carlson, D.K. (2001), ``Majority of users express concern about Internet privacy, Gallup Poll, available at: releases/pr asp (accessed 28 June 2001). Katona, G. (1953), ``Rational behavior and economic behavior, Psychological Review, pp Liebermann, Y. and Paroush, J. (1982), ``Economic aspects of diffusion processes, Journal of Economics and Business, Vol. 34, pp Mitchell, V.W., Davies, F., Moutinho, L. and Vassos, V. (1999), ``Using neural networks to understand service risk in the holiday product, Journal of Business Research, Vol. 46, pp Miyazaki, A.D. and Fernandez, A. (2001), ``Consumer perceptions of privacy and security risks for online shopping, The Journal of Consumer Affairs, Vol. 35 No. 1, pp Morris, M.G. and Turner, J.M. (2001), ``Assessing users subjective quality of experience with the World Wide Web: an exploratory examination of temporal changes in technology acceptance, International Journal of Human Computer Studies, Vol. 54 No. 6, pp NielsenNetRatings (2001a), ``Average Web usage, available at: NRpublicreports.usagemonthl y (accessed 22 October 2001). NielsenNetRatings (2001b), ``Internet usage statistics, available at: NRpublicreports.usagemonthl y (accessed 22 October 2001). Pew Internet and American Life Project (2000), ``Trust and privacy online: why Americans want to rewrite the rules, available at: toc.asp?report=19 (accessed 17 July 2002). Pew Research Center (1998), ``The Internet news audience goes ordinary, available at: tech98sum.htm (accessed 17 July 2002). Salkin, S. (1999), ``Fear of buying, Logistics Management and Distribution Report, Vol. 38 No. 5, p Sevcik, P. and Bartlett, J. (2001), ``Understanding Web performance, Business Communications Review, Vol. 31 No. 10, pp Silverman, B.G., Bachann, M. and Al-Akharas (2001), ``Implications of buyer decision theory for design of e-commerce Web sites, International Journal of Human Computer Studies, Vol. 55, pp Thibodeaux, T. (2000), ``Online shoppers defined, Dealerscope, December, p. 8. Vividence Corporation (2001), ``Holiday readiness 2001: helping retailers prepare for the holiday season, Vividence Research Report, available at: 300