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The census bureau of the department of commerce reported in 2010 that total retail sales
for the first quarter of 2010 were estimated at $960 billion and the estimate of U.S. retail e-commerce
sales for the first quarter of 2010 was $38.7 billion, which means online represents
24% of total retails of the U.S. (U.S. Census Bureau News, 2010).
Despite this remarkable growth in sales, there is evidence to suggest that many consumers
shop at retail websites with intentions to purchase but subsequently do not complete the
transaction. A browser is defined as an individual who searches and examines websites to
get more product information with the possible intention of purchasing using the Internet
(Lee & Johnson, 2002). Research has noted three primary reasons why people have not
completed an on-line retail transaction. First, 35% of the shoppers fail to complete the
transaction, not because they do not want to buy but because of technical problems, including
computer freezes, disconnections, or service interruptions (Shop.org, 2001). Second, some
consumers are just trying the Internet shopping experience without any intention of making
a purchase. These consumers use the online store as a tool to “window shop- which is gath-ering
information and screening” merchandise with the ultimate intent to purchase the product
in a brick-and-mortar store. Third, still other consumers start filling a cart but then leave the
cart and the site without completing the transaction (Fram & Grandy, 1997). It is the last
two groups, those who have no current intention of buying and those who abandon their
carts, which are most often studied to determine why they have not made an online purchase.
Reasons found for consumers to start filling a cart but then to leave the cart and the site
without completing the transaction included (a) lack of credit card security and privacy
protection, (b) technical problems, (c) difficulty in finding specific products, (d) unacceptable
delivery fees and methods, (e) inadequate return policies, (f) lack of personal service, (g)
inability to use sensory evaluation, (h) negative Internet shopping experiences, and (i) slow
download speeds (Eastlick & Lotz, 1999; Kim, Kim, & Kumar, 2003; Kwon & Lee, 2003;
Lee & Johnson, 2002; Watchravesringkan & Shim, 2003).
In trying to understand the reasons for non-completed transactions, Fishbein and Ajzen’s
(1975) theory of reasoned action is often used to study how an individual’s attitudes toward
online shopping will influence that person’s behavioral intention (Shim et al., 2001). In the
model, attitude has been viewed as a predictor of intention and, finally, actual behavior. This
study also applied their model to gain more understanding into this consumer behavior pattern.
Purpose of the Study
The purpose of the study was to explore the attitude differences towards Internet shopping
among four groups of web shoppers: the current non-web shopper, the user who visits web
stores with no intention to buy, the browser who has an intention to purchase online but has
never done so, and the person who has made an online purchase with the intention of predict-ing
the purchasing behavior of each of these groups on the Internet. The research focused
on understanding differences among the four groups in terms of attitudes towards online
shopping, intention, and purchasing experiences. Fishbein and Ajzen’s (1975) theory of
reasoned action as applied to consumers’ online shopping analysis.
Theoretical Framework
Two theoretical models, the Theory of Reasoned Action (Fishbein & Ajzen, 1975) and the
Diffusion of Innovations Theory (Rogers, 1995), offered guidance in formulating a research
framework. Fishbein and Ajzen’s (1975) Theory of Reasoned Action (TRA) provides a be-
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havioral explanation of the importance of attitudes on a prospective buyer’s decision-making
process. The central tenet of the TRA is that humans behave in a reasoned manner trying to
obtain favorable outcomes while meeting the expectations of others. The TRA attempts to
explain how attitudes are formed and how and why such attitudes affect the way people act.
Fishbein and Ajzen proposed that a person’s behavior is determined by the intention to per-form
the behavior. Intentions are a function of the individual’s attitudes towards the behavior
and the resultant outcome. Ajzen (1991) later defined attitudes as an individual’s feeling,
either positive or negative, that performance of the behavior will lead to the desired outcome.
Intentions are assumed to capture the motivational factors that influence a behavior and can
measure the amount of effort someone is willing to exert when performing a behavior.
When applying the TRA to consumer behavior, consumers are believed to have a certain
level of intention for each alternative selection (Shim et al, 2001; Watchravesringkan &
Shim, 2003). The alternative selected will be that with the highest perceived reward value
(Fishbein & Ajzen, 1975). TRA’s attitude-intention-behavior continuum model is the most
frequently applied theory to explain consumer behavior. In this study, TRA was used to ex-amine
the individual’s attitude as a predictor of intention and then intention as a predictor
of behavior.
While the TRA provides a behavioral explanation of attitudes on the decision-making
process, Rogers’ (1995) Diffusion of Innovations Theory (DIT) provides a sociological ap-proach
to innovation and adoption. The DIT states that innovation is a process communicated
through formal and informal channels over time among members in social systems. In this
study, the innovation is online shopping. The application of the DIT to this study provided
the conceptual framework to show that each of four categories of consumers would exhibit
common characteristics at the respective stages in which they had embraced internet shopping.
The DIT model would suggest consumers in the same category, non-web shopper, web-store
visitor, Internet browser, or Internet buyer, should share some characteristics (e.g., level of
Internet experience).
Rogers (1995) divided the adoption process into five stages: knowledge, persuasion, de-cision-
making, implementation, and confirmation. DIT theory has been applied to research
on consumer behavior as an explanation of the movement of new ideas, practices, and
products through a social system. Research has addressed the consumers’ intent to buy,
which covers the first three stages of the model (Shim et al, 2001; Liang & Huang, 1998)
and considers intention of the consumer. This study attempts to evaluate the last three stages
of “the adoption process” (decision-making, implementation, and confirmation) to analyze
the Internet-buying behavior of consumers.
By using the TRA model which assumes online buying behavior is a function of attitude,
the various parts of people’s overall attitudes based on research can be put into a hypothesized
model of Internet buying. As shown in Figure 1, illustrates the resultant framework used for
this research to predict online buying behavior.
Research Methods
To test the theoretical intention and behavior model, this study categorized the individual
variables, described in Figure 1 into the following three general collective factors: (1) mar-keting,
(2) consumer, and (3) technology. These were used parsimoniously to compare the
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attitude and intention of non-web shoppers, web-store visitors, Internet browsers, and Internet
buyers.
The consumer factor comprised sub-factors that influence consumers’ feelings and attitudes
but that are outside of their control. The marketing factor was developed based on marketing
4P ( price, product, place, and promotion) related variables. The technology factor was
constructed with sub-factors that are related to computer and Internet environments and that
are outside of the consumer’s control. Scores from these three collective factors were calcu-lated
by summing the scores of each of the underlying sub-factors.
Research Sampling
Two hundred sixty-six college students in the U.S. served as a purposive sampling. Using
college students allowed the researchers to focus on the group called Generation Y, individuals
who are showing high levels of online shopping and buying and represent a tremendous future
potential market over their lifetime (Vogt, 1998). Several studies suggested that college
students were often users of technology in general and likely to buy products online (Bruin
& Lawrence, 2000; Norum, 2008). Students represent over seventy billion dollars in buying
power today (Forrester Research, 2006). Their higher than average levels of education can
be expected to generate high levels of disposable income, making future online purchases
even more likely (Norum, 2008).
Data Collection and Analysis
Three universities from central United States were identified for data collection. At each
university, a faculty member was identified and contacted requesting participation in the
survey. At each university, surveys were provided along with a cover letter, informed consent
script, and scantrons forms. Data was collected from three 343 respondents for analysis. The
data was cleaned by deleting those respondents where data was missing on important questions
such as a respondent’s previous online experience and intention to purchase products online.
These deletions reduced the sample size to 266 respondents (n=266).
Using Cronbach’s alpha scores, the reliability of the hypothesized factors will be examined.
Analysis of the hypotheses was performed using ANOVAs, chi-squares, and logistic regres-sion.
The data analysis stage was divided into four phases. Phase I classified the respondents
into four consumer groups based on their Internet-using experience as determined by their
answers to survey question 73: “When thinking of my use of the Internet for shopping and/or
buying, typically I am a Non-Web shopper, Web-store visitor (look for general product in-formation
only), Internet browser (look for specific information but would not buy online),
or Internet buyer (look for specific product information and would buy/have bought online).”
Descriptive statistics (frequency analysis, means score, and chi-square analyses) were used
to compare and describe the demographic background and the technology and Internet ex-perience
of the respondents.
Phase II involved the testing of the theoretical model and examination of the internal reli-abilities
of the items measuring the theoretical concepts through use of Cronbach’s alpha
coefficients. Phase III included comparisons among the four consumer groups’ attitudinal
differences in terms of the consumer, marketing, and technology factors of online shopping.
Phase IV involved analyzing the differences between the four groups of consumers (non-
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5. JONGEUN KIM
web shopper, web-store visitor, Internet browser, and Internet buyer) on various variables
including demographic background, technology and Internet experiences, and consumer,
marketing, and technology factors. In this stage, logistic regression analysis was used to
predict the consumers’ intention to purchase on the Internet.
Results
There were significant differences among the four consumer groups’ demographic back-grounds.
The marital status of the respondents (F (3, 266) = 9.64) and the number of credit
cards used showed significant differences (F (3, 266) = 15.33) for the demographic back-ground.
There were no significant differences among the four groups in terms of age, gender,
ethnicity, income, self-support, and residence. Ninety-two percent of the non-web shoppers
were single while 91% of the web-store visitors and 75% of the Internet browsers were
single. Finally, 88% of Internet buyers were single and 12% were married. Seventy-eight
percent of the Internet buyers had one or more credit cards compared to 66% of Internet
browsers, 56% of web store visitors, and 46% of the non-web shoppers. [See Table 1.]
Figure 1: Theoretical Research Framework
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Table 1: Demographic Differences among Four Consumer Groups
Internet Internet Buyer χ2
Browser
Web-store
Visitor
Non-web
Shopper
Total
(n=266)
Demographic Category
(n=99)
(n=13) (n=66) (n= 88)
Age 18-20 yrs 58 (21.8%) 6 (46.2%) 16 (46.2%) 20 (22.7%) 16 (16.2%) 6.81
21-23 yrs 136 (51.1%) 5 (38.5%) 32 (48.5%) 44 (50.0%) 55 (55.6%)
24 yrs 72 (27.1%) 2 (15.4%) 18 (27.3%) 24 (27.3%) 28 (28.3%)
Gender Male 115 (43.2%) 4 (30.8%) 31 (47.0%) 41 (46.6%) 39 (39.4%) 2.20
Female 151 (56.8%) 9 (69.2%) 35 (53.0%) 47 (56.4%) 60 (60.6%)
Ethnicity White 213 (80.1%) 11 (84.6%) 57 (86.4%) 68 (77.3%) 77 (77.8%) 2.56
Others 53 (19.9%) 2 (15.4%) 9 (13.6%) 20 (22.7%) 22 (22.2%)
Marital status Married 41 (15.4%) 1 (7.2%) 6 (9.1%) 22 (25.0%) 12 (12.1%) 9.64*
Single 225 (84.6%) 12 (92.3%) 60 (90.9%) 66 (75.0%) 87 (87.9%)
Income No income 47 (17.7%) 4 (30.8%) 13 (19.7%) 17 (19.3%) 13 (13.1%) 3.73
$1 – 500 98 (36.8%) 5 (38.5%) 23 (34.8%) 31 (35.2%) 39 (39.4%)
$501 + 121 (45.5%) 4 (30.8%) 30 (45.5%) 40 (45.5%) 47 (47.5%)
Self support Yes 106 (39.8%) 4 (30.8%) 35 (53.0%) 33 (37.5%) 34 (34.3%) 6.67
No 160 (60.2%) 9 (69.2%) 31 (47.0%) 55 (62.5%) 65 (65.7%)
Credit card None 88 (33.1%) 7 (53.8%) 29 (43.9%) 30 (34.1%) 22 (22.2%) 15.33*
1 -2 147 (55.2%) 5 (38.5%) 31 (47.0%) 44 (50.0%) 67 (67.7%)
3+ 31 (11.7%) 1 (7.7%) 6 (9.1%) 14 (15.9%) 10 (10.1%)
Residencea On campus 47 (17.7%) 4 (30.8%) 10 (15.6%) 11 (12.5%) 22 (22.2%) 10.85
Off campus 219 (82.3%) 9 (69.2%) 54 (81.8%) 77 (87.5%) 77 (77.8%)
Data displayed as n (%).
a Two web-store visitors were missing on residence variable.
*p < .05.
In the comparison of technology and Internet experiences, there were significant differences
among the four consumer groups in terms of years of computer use (F (3, 266) = 21.18) and
hours of Internet use (F (3, 266) = 15.52). There were no significant differences among the
four groups in terms of years of Internet usage, ability of Internet usage, methods of Internet
access, speed of Internet access, and the primary Internet usage. [See Table 2.]
Cronbach’s Alpha for the Theoretical Model
To assess internal consistency (reliability) of the items for each of the three theoretical factors,
Cronbach’s Alpha was computed on the items on each factor to assess the ability of the items
to measure the same concept. Cronbach’s Alpha coefficients for the theoretical concepts are
provided in Table 3. The consumer factor score was .81, exceeding the standard level of .7
(Stevens, 2002) while the marketing factor had a marginally acceptable alpha value of .65
(Tseng et al., 2000). The items on the technology factor, however, demonstrated low internal
consistency with a coefficient of only .46. In further exploratory analysis of the individual
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7. JONGEUN KIM
technology items (results not reported here), none of the items showed any significant or
substantial exploratory power. Therefore all of these questions and responses were deleted.
Table 2: Technology and Internet Use Experience Comparison for Four Consumer
Groups
Tech. Category
Experience Visitor Browser Buyer
Non-Web Web-Store Internet Internet χ2
Shopper
Total
(n=266)
(n=13) (n=66) (n= 88) (n=99)
Computer < 3 yrs 27 (10.1%) 1 (7.7%) 7 (10.6%) 12 (13.6%) 7 (7.7%) 23.18*
use 4-6 yrs 67 (25.2%) 4 (30.8%) 26 (39.4%) 16 (18.2%) 21 (21.2%)
< 7 yrs 172 (64.7%) 8 (61.5%) 33 (50.0%) 60 (68.2%) 71 (71.7%)
Internet < 3 yrs 45 (16.9%) 1 (7.7%) 12 (18.1%) 17 (19.3%) 15 (15.2%) 10.97
Use > 7 yrs 221 (83.1%) 12 (92.3%) 54 (81.8%) 71 (80.7%) 84 (84.8%)
Ability to use Some skillful 54 (20.3%) 5 (38.5%) 14 (21.2%) 17 (19.3%) 18 (18.2%) 3.01
Internet Skillful 212 (79.7%) 8 (61.5%) 52 (78.8%) 71 (80.7%) 81 (81.8%)
Internet Private 262 (98.5%) 13 (100%) 63 (95.5%) 87 (98.9%) 99 (100 %) 5.91
access Public 4 (1.5%) 0 (0.0%) 3 (4.5%) 1 (1.1%) 0 (0.0 %)
Speed of Slow (Dial up) 77 (28.9%) 7 (53.8%) 21 (31.8%) 24 (27.3%) 25 (25.3%) 4.96
Fast (DSL, 189 (71.1%) 6 (46.2%) 45 (68.2%) 64 (72.7%) 74 (74.7%)
etc.)
Internet
Hours of < 3 hrs 88 (33.1%) 5 (38.5%) 30 (45.5%) 32 (36.4%) 21 (21.2%) 15.52*
Internet use 4-10 hrs 123 (46.2%) 8 (61.5%) 26 (39.4%) 38 (43.2%) 51 (51.5%)
< 11 hrs 55 (20.7%) 0 (0.0%) 10 (15.2%) 18 (20.5%) 27 (27.3%)
Primary Search&shop 71 (26.7%) 1 (7.7%) 21 (31.8%) 19 (21.6%) 30 (30.3%) 16.87
Communica- 139 (52.3%) 12 (92.3%) 37 (56.1%) 43 (48.9%) 47 (47.5%)
tion
Internet use
56 (21.0%) 0 (0.0%) 8 (12.1%) 26 (29.5%) 22 (22.2%)
Entertainment
Data displayed as n (%).
*p < .05.
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Table 3: Cronbach’s α Values for Hypothesized Model
Theoretical Factor Subscale Cronbach’s α (0<α<1)
Consumer Factor Privacy .25
Security .29
Time saving .73
Ease of use .51
Convenience .58
Enjoyment .66
Company reputation .46
Previous experience .28
Tactility .17
Consumer Factor Scale Score .81
Marketing Factor Price .22
Product .24
Promotion .70
Delivery .09
Return .04
Customer service .36
Marketing Factor Scale Score .65
Technology Factor Internet access .09
Download time .18
Representation .43
Technology Factor Scale Score .46
Findings indicate it is possible to measure collectively respondents’ consumer and marketing
attitudes as a single factor. This finding offers greater parsimony in model building, thus
improving statistical testing. Not only do the sub-factors hold together as a scale but they
also moderately correlate with each other. One score can replace the nine underlying indi-vidual
sub-factors found within the consumer area or the six sub-factors in the marketing
area.
Attitudinal Differences on Internet Shopping for Four Consumer Groups
For analysis of the attitudinal differences toward Internet shopping between the four groups,
the one-way analysis of variance (ANOVA) test was adopted to indicate that the four groups
of consumers were significantly different in their attitudes towards the consumer factor (F
(3, 266) = 42.09) and marketing factors (F (3, 266) = 13.22) involved with Internet shopping.
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Even though web-store visitors and Internet browsers exhibited positive attitudes toward
the use of the Internet as an alternative shopping tool, still all three of the parsimonious attitude
scores for the consumer and marketing factors were lower than those of Internet buyers. The
higher the score as their attitudes on the consumer and marketing factors of online shopping,
the more likely consumer were to be Internet buyers. [See Table 4.]
Table 4: Attitudinal Differences for Four Consumer Groups
Factor Non-Web Shopper Web-Store Visitor Internet Browser Internet Buyer
(n=13) (n=66) (n=88) (n=99)
Mean SD Mean SD Mean SD Mean SD F
Consumer Factor Score 56.2 5.7a 61.0 8.1ab 64.2 10.4b 74.7 9.0c 42.09**
Marketing Factor
34.8 6.3a 38.6 5.2ab 39.2 5.1b 42.1 4.4c 13.22**
Score
a…c Different superscripts denote significant differences between groups by Tukey’s post
hoc analyses.
**p < .0001.
Prediction of the Consumers’ Intention to Purchase on the Internet
Internet shopping intention was regressed on consumer factors, years of computer use, and
Internet access methods. These three predictors accounted for just above half of the variance
in shopping intention scores (R 2 = .538), which was highly significant, (F (3, 266) = 8.704,
p < .05.). A linear regression analysis revealed that consumer factor score, (β = .074, p =
.000), years of computer use, (β = .505, p = .000), and methods of access to the Internet, (β
= 1.219, p = .034) were highly significant predictors. Overall, the consumer factor showed
a strong relationship in predicting online purchasing intention and behavior while the mar-keting
factor showed only a moderate relationship. The consumer factor was not only signi-ficant
among the four groups but was also significant throughout the study in terms of pre-dicting
who intends to buy online and who actually does buy online. The marketing factor
showed little predictive ability in this study. This may have been influenced by the weak
relationship identified by the moderate alpha coefficient. The technology sub-factors did not
hold together at all as a single factor. This may be related to the study subjects, the vast
majority of whom exhibited high technology use and experience. [See Table 5.]
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Table 5: Prediction of Intention to Purchase Online
Predictor β p
Consumer Factor Score .074 .000*
Marketing Factor Score -.010 .543
Age -.073 .507
Gender .029 .834
Ethnicity .205 .256
Marital status .013 .949
Income -.091 .931
Self-support -.254 .106
Number of credit cards -.098 .371
Residence -.131 .445
Years of computer use .505 .000*
Years of Internet use -.375 .104
Internet use ability .031 .870
Access to Internet 1.219 .034*
Speed of the Internet -.152 .316
Hours of Internet use .184 .069
Primary usage of Internet -.115 .253
R 2 = .538.
F = 8.704.
*p < .05.
Conclusions and Suggestions
Findings of the study suggest that Internet retailers should provide convenience, secure
transactions, and a complete product description as well as ample visual presentations of
merchandise. Retailers should also provide an enjoyable atmosphere in order to make Internet
shopping advantageous over other retail outlets. Also, successful e-tailers will respond to
the individual needs of each group if they desire to move them from non-web shoppers, web-store
visitors, and Internet browsers to Internet buyers.
The purpose of this study is to increase the number and frequency of online purchases.
The data provide specific insights as to how each group of shopper differs in their attitudes
about buying products online. Such insights offer e-tailers and business owners suggestions
on how to reach each segment more effectively and perhaps move them into Internet buyers.
The study supports the idea of classifying the consumer’s status in terms of making an
online purchase. From such classification, more specific recommendations are proposed,
such as to offer online demonstrations in stores for non-web shoppers or to focus on creating
a site that attracts web-visitors to spend more time. For browsers, privacy and security pro-
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11. JONGEUN KIM
tection statement, discounts and free shipping offers may be the keys. For existing buyers,
understanding what they buy and then making the online purchase quicker and providing
more information may be possible tactics to ensure an actual purchase.
Not only does this study provide guidance to the e-tailer who is trying to encourage more
online buying with research findings of online shoppers’ attitude and intention analysis to-wards
online shopping but also the finding of this study contributes to the consumer behavior
literature in four ways. First, it groups the most frequently cited variables in the literature
into three parsimonious factors. These factors were then tested, and it was confirmed that
the consumer factor is most influential. Second, the study confirms that individual attitudes
are predictor of intention, supporting the finding of Shim et al.’s (2001) study and goes one
step further by offering that the individual’s intention to purchase online is a predictor of
purchasing behavior. Finally, the data adds to the literature by providing that consumers can
be categorized based on their online shopping experiences into the following four groups:
non-web shoppers, web-store visitors, Internet browsers, and Internet buyers. Each of these
groups can be separately distinguished and analyzed as to their profiles and why each has
or has not yet adopted online buying as a behavior.
Finally, the current study has several contributions for consumer educators by providing
a framework by which they may apply the Theory of Reasoned Action (TRA) in their own
classes to other real-life consumer applications. TRA provides a behavioral explanation of
the importance of attitudes on a prospective buyer’s decision-making process and explains
the sequence of the human behavioral process from attitude, intention and behavior. By
having an example of how to apply TRA, educators can explain to students that there is a
rational sequential decision making process in virtually all consumer transactions.
Second, consumer educators will better understand the benefit of classifying a subject
research group into smaller sub-groups to better serve the needs of the subject group. For
example, the researcher grouped target consumers into four groups based on their character-istics
instead of analyzing the single group of online consumers.
References
ACNielsen (2007). Seek and You Shall Buy. Entertainment and Travel. viewed 18 January 2007
< http://www2acnielsen.com/news/20051019.shtml>.
Ajzen, I. (1991). The theory of planned behavior: Some unresolved issues. Organizational Behavior
and Human Decision Process, 50, 179-211.
Bruins,M & Lawrence, F (2000). Differences in spending habits and credit use of college students.
Journal of Consumer Affairs, 34, 113 - 133
Easterling, Cynthia; Loyd, Dolly; and Lester, Deborah. “Internet Shopping Experiences: College Stu-dents’
Perceptions,” Proceedings published by the Atlantic Marketing Association, 2002.
Eastlick, M. A., & Lotz, S. (1999). Shopping motives for mail catalog shoppers. Journal of Business
Research, 45, 281-299.
eMarketer TM Digital Intelligence. College students online: driving change in Internet and mobile
usage. Retrieved September 2008, from http://www.emarketer.com/Report.aspx?code=
emarketer
Fishbein, M. A., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory
and research. Reading, MA: Addison-Wesley.
Forrester Research (2006). Online Retail: Strong, Broad Growth, viewed January 2007.
<http://www.forester.com/Research/Document/Excerpt/0,7211,39915,00.html.
375
12. THE INTERNATIONAL JOURNAL OF INTERDISCIPLINARY SOCIAL SCIENCES
Fram, E. H., & Grandy, D. B. (1997). Internet shoppers: Is there a surfer gender gap? Direct Marketing,
59, 46-50.
Kim, Y., Kim, E. Y., & Kumar, S. (2003). Testing the behavioral model of online shopping for clothing.
Clothing and Textiles Research Journal, 21, 32-40.
Kwon, K., & Lee, J. (2003). Concerns about payment security on Internet purchases: A perspective
on current on-line shoppers. Clothing and Textiles Research Journal, 21, 174-184.
Lee, M., & Johnson, K. K. P. (2002). Exploring differences between Internet apparel purchasers,
browsers and non-purchasers. Journal of Fashion Marketing and Management, 6, 146-157.
Lester, Deborah; Loyd, Dolly; and Easterling, Cynthia. “Generation X and Y: Attitudes and Behavior
Towards Internet Shopping,” Proceedings published by the Atlantic Marketing Association,
2003.
Liang, T. P., & Huang, J. S. (1998). An empirical study on consumer acceptance of products in elec-tronic
markets: A transaction cost model. Decision Support Systems, 24, 29-43.
Norum, P. S. (2008). Student Internet purchase. Family and Consumer Sciences Research Journal,
36, 373-388.
Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: The Free Press.
Shim, S., Eastlick, M. A., Lotz, S. L., & Warrinton, P. (2001). An online prepurchase intentions
model: The role of intention to search. Journal of Retailing, 77, 397–416.
Shop.org. (2001). Shop.Org. Press Room. Washington, D.C.: National Retail Federation. [Online].
Available: http://www.shop.org.
Stevens, J. (2002). Applied multivariate statistics for social sciences (4th ed.). Mahwah, NJ: Lawrence
Herubaun Associates.
Tseng, M., DeVellis, R. F., Kohlmeier, L., Khare, M., Maurer, K. R., Everhart, J. E., & Sandler, R. S.
(2000). Patterns of food intake and gallbladder disease in Mexican Americans. Public Health
Nutrition, 3, 233-243.
U.S. Census Bureau News.[Online] (2010). Available: http://www.census.gov/retail/mrts/www/data/
pdf/10Q1.pdf
Watchravesringkan, K., & Shim, S. (2003). Information search and shopping intentions through Internet
for apparel products. Clothing and Textile Research Journal, 21, 1-7.
About the Author
Dr. Jongeun Kim
Jongeun Kim, Ph.D., is an Assistant Professor of Apparel Design and Merchandising at
California State University, Northridge. She received her B.S. in Sociology from Ewha
Women’s University in Seoul, Korea and earned a second B.S. and her M.A. both in Apparel
Design and Merchandising from Kon-Kuk University in Seoul, Korea. She received her
Ph.D in Human Environmental Sciences from Oklahoma State University in Stillwater. Kim
has been teaching in higher education for over 10 years and has developed courses in apparel
design, fashion theory, the culture and psychology of fashion, special needs/functional
clothing and apparel and textiles in the global economy. Kim’s research focuses on consumer
behavior, e-commerce and m-commerce marketing, sustainability and eco and green fashion.
Kim has presented her work at national and international conferences, published her research
in journals and conference proceedings and organized workshops and seminars sponsored
by professional associations such as ITAA (International Textile and Apparel Association),
AAFCS (American Association of Family and Consumer Sciences), HIC (Hawaiian Interna-tional
Conference) and AERA (American Educational Research Association).
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