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Analyzing College Students’ Online Shopping Behaviorthrough Attitude and IntentionJongeun Kim, California State University, Northridge, California, USA

Abstract: This exploratory study examined attitudinal differences among college students on Internetshopping. College students were classified as non-web shoppers, web-store visitors, Internet browsers,and Internet buyers based on their previous Internet shopping experience. The model identified thetheoretical factors, grouped into the three general categories of consumer, marketing, and technologythat influence the online shopping of these four groups. Significant demographic background differencesin terms of marital status, number of credit cards held, hours of Internet use, and primary use of theInternet were found among the four consumer groups. The attitudes and intentions of these four con-sumer groups towards online shopping were analyzed by using ANOVA. For four groups of consumers(non-web shopper, web-store visitor, Internet browser, and Internet buyer) on various variables includ-ing demographic background, technology and Internet experiences, and consumer, marketing, andtechnology factors were examined by using regression analysis to predict consumers’ future intentionto purchase on the Internet. The key finding of the study was that the consumer factor, comprised ofprivacy, security and trust, time saving, ease of use, convenience, enjoyment provided by shopping,company reputation and tactility, was most significant for who intended to purchase online and whodid buy online. The paper describes the study and concludes by highlighting contributions to e-tailersand business owners and the theoretical framework in the study will be utilized by consumer educators.

Keywords: Consumer Behavior, College Students, Theory of Reasoned Action, Internet Shopping, E-commerce, Fishbein and Ajzen

TODAY’S WEB-SAVVY COLLEGE students represent current and future targetsfor e-commerce companies. College student internet users represent over $17.8 billionin 2009. In 2008, 95.7% of college students went online at least once a month. Theyare the most digitally connected demographic groups in the U. S. (eMarketer, 2008).

They represent a significant part of the online buying consumer and will be a long-term po-tential market. Today’s student represents a generation of Americans born between 1977and 1994 that is referred to in the media as Generation Y. They are known as the millennialgeneration which represents seventy-two million Americans as large as the baby boomgeneration. The tech-savvy Generation Y population has embraced anything wired (Lester,et al, 2003; Mitchell, 1998; Weiss, 2003). The love of technology along with the higher thanaverage levels of education that when paired with the expected high levels of disposable in-come make understanding this substantial online market important , (Norum, 2008).

The Internet has captivated the attention of retail marketers. The Internet, as a retail outlet,has moved from its infancy, used by only a few, to a market with significant purchasingpower. Buying on the Internet has become one of the most rapidly growing modes of shop-ping, demonstrating significant annual sales increases in recent years due to the Internet’saccessibility and ability to provide large amounts of information quickly and inexpensively.

The International Journal of Interdisciplinary Social SciencesVolume 5, Number 3, 2010, http://www.SocialSciences-Journal.com, ISSN 1833-1882© Common Ground, Jongeun Kim, All Rights Reserved, Permissions:[email protected]

The census bureau of the department of commerce reported in 2010 that total retail salesfor 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 represents24% 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 consumersshop at retail websites with intentions to purchase but subsequently do not complete thetransaction. A browser is defined as an individual who searches and examines websites toget more product information with the possible intention of purchasing using the Internet(Lee & Johnson, 2002). Research has noted three primary reasons why people have notcompleted an on-line retail transaction. First, 35% of the shoppers fail to complete thetransaction, not because they do not want to buy but because of technical problems, includingcomputer freezes, disconnections, or service interruptions (Shop.org, 2001). Second, someconsumers are just trying the Internet shopping experience without any intention of makinga 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 productin a brick-and-mortar store. Third, still other consumers start filling a cart but then leave thecart and the site without completing the transaction (Fram & Grandy, 1997). It is the lasttwo groups, those who have no current intention of buying and those who abandon theircarts, 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 sitewithout completing the transaction included (a) lack of credit card security and privacyprotection, (b) technical problems, (c) difficulty in finding specific products, (d) unacceptabledelivery 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) slowdownload 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 towardonline shopping will influence that person’s behavioral intention (Shim et al., 2001). In themodel, attitude has been viewed as a predictor of intention and, finally, actual behavior. Thisstudy 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 shoppingamong four groups of web shoppers: the current non-web shopper, the user who visits webstores with no intention to buy, the browser who has an intention to purchase online but hasnever 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 focusedon understanding differences among the four groups in terms of attitudes towards onlineshopping, intention, and purchasing experiences. Fishbein and Ajzen’s (1975) theory ofreasoned action as applied to consumers’ online shopping analysis.

Theoretical FrameworkTwo theoretical models, the Theory of Reasoned Action (Fishbein & Ajzen, 1975) and theDiffusion of Innovations Theory (Rogers, 1995), offered guidance in formulating a researchframework. 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-makingprocess. The central tenet of the TRA is that humans behave in a reasoned manner trying toobtain favorable outcomes while meeting the expectations of others. The TRA attempts toexplain 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 behaviorand 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 canmeasure 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 certainlevel 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 mostfrequently 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 predictorof behavior.

While the TRA provides a behavioral explanation of attitudes on the decision-makingprocess, Rogers’ (1995) Diffusion of Innovations Theory (DIT) provides a sociological ap-proach to innovation and adoption. The DIT states that innovation is a process communicatedthrough formal and informal channels over time among members in social systems. In thisstudy, the innovation is online shopping. The application of the DIT to this study providedthe conceptual framework to show that each of four categories of consumers would exhibitcommon 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-storevisitor, Internet browser, or Internet buyer, should share some characteristics (e.g., level ofInternet experience).

Rogers (1995) divided the adoption process into five stages: knowledge, persuasion, de-cision-making, implementation, and confirmation. DIT theory has been applied to researchon consumer behavior as an explanation of the movement of new ideas, practices, andproducts 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 stagesof “the adoption process” (decision-making, implementation, and confirmation) to analyzethe 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 hypothesizedmodel of Internet buying. As shown in Figure 1, illustrates the resultant framework used forthis research to predict online buying behavior.

Research MethodsTo test the theoretical intention and behavior model, this study categorized the individualvariables, 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 Internetbuyers.

The consumer factor comprised sub-factors that influence consumers’ feelings and attitudesbut that are outside of their control. The marketing factor was developed based on marketing4P ( price, product, place, and promotion) related variables. The technology factor wasconstructed with sub-factors that are related to computer and Internet environments and thatare 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 SamplingTwo hundred sixty-six college students in the U.S. served as a purposive sampling. Usingcollege students allowed the researchers to focus on the group called Generation Y, individualswho are showing high levels of online shopping and buying and represent a tremendous futurepotential market over their lifetime (Vogt, 1998). Several studies suggested that collegestudents 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 buyingpower today (Forrester Research, 2006). Their higher than average levels of education canbe expected to generate high levels of disposable income, making future online purchaseseven more likely (Norum, 2008).

Data Collection and AnalysisThree universities from central United States were identified for data collection. At eachuniversity, a faculty member was identified and contacted requesting participation in thesurvey. At each university, surveys were provided along with a cover letter, informed consentscript, and scantrons forms. Data was collected from three 343 respondents for analysis. Thedata was cleaned by deleting those respondents where data was missing on important questionssuch 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 respondentsinto four consumer groups based on their Internet-using experience as determined by theiranswers to survey question 73: “When thinking of my use of the Internet for shopping and/orbuying, 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 usedto 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 alphacoefficients. Phase III included comparisons among the four consumer groups’ attitudinaldifferences 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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web shopper, web-store visitor, Internet browser, and Internet buyer) on various variablesincluding demographic background, technology and Internet experiences, and consumer,marketing, and technology factors. In this stage, logistic regression analysis was used topredict the consumers’ intention to purchase on the Internet.

ResultsThere 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 creditcards 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 shopperswere single while 91% of the web-store visitors and 75% of the Internet browsers weresingle. Finally, 88% of Internet buyers were single and 12% were married. Seventy-eightpercent of the Internet buyers had one or more credit cards compared to 66% of Internetbrowsers, 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 Groupsχ2Internet BuyerInternet

BrowserWeb-storeVisitor

Non-webShopper

Total(n=266)

CategoryDemographic(n=99)

(n= 88)(n=66)(n=13)

6.8116 (16.2%)20 (22.7%)16 (46.2%)6 (46.2%)58 (21.8%)18-20 yrsAge55 (55.6%)44 (50.0%)32 (48.5%)5 (38.5%)136 (51.1%)21-23 yrs28 (28.3%)24 (27.3%)18 (27.3%)2 (15.4%)72 (27.1%)24 yrs

2.2039 (39.4%)41 (46.6%)31 (47.0%)4 (30.8%)115 (43.2%)MaleGender60 (60.6%)47 (56.4%)35 (53.0%)9 (69.2%)151 (56.8%)Female

2.5677 (77.8%)68 (77.3%)57 (86.4%)11 (84.6%)213 (80.1%)WhiteEthnicity22 (22.2%)20 (22.7%)9 (13.6%)2 (15.4%)53 (19.9%)Others

9.64*12 (12.1%)22 (25.0%)6 (9.1%)1 (7.2%)41 (15.4%)MarriedMarital status87 (87.9%)66 (75.0%)60 (90.9%)12 (92.3%)225 (84.6%)Single

3.7313 (13.1%)17 (19.3%)13 (19.7%)4 (30.8%)47 (17.7%)No incomeIncome39 (39.4%)31 (35.2%)23 (34.8%)5 (38.5%)98 (36.8%)$1 – 50047 (47.5%)40 (45.5%)30 (45.5%)4 (30.8%)121 (45.5%)$501 +

6.6734 (34.3%)33 (37.5%)35 (53.0%)4 (30.8%)106 (39.8%)YesSelf support65 (65.7%)55 (62.5%)31 (47.0%)9 (69.2%)160 (60.2%)No

15.33*22 (22.2%)30 (34.1%)29 (43.9%)7 (53.8%)88 (33.1%)NoneCredit card67 (67.7%)44 (50.0%)31 (47.0%)5 (38.5%)147 (55.2%)1 -210 (10.1%)14 (15.9%)6 (9.1%)1 (7.7%)31 (11.7%)3+

10.8522 (22.2%)11 (12.5%)10 (15.6%)4 (30.8%)47 (17.7%)On campusResidencea77 (77.8%)77 (87.5%)54 (81.8%)9 (69.2%)219 (82.3%)Off campus

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 differencesamong the four consumer groups in terms of years of computer use (F (3, 266) = 21.18) andhours of Internet use (F (3, 266) = 15.52). There were no significant differences among thefour groups in terms of years of Internet usage, ability of Internet usage, methods of Internetaccess, speed of Internet access, and the primary Internet usage. [See Table 2.]

Cronbach’s Alpha for the Theoretical ModelTo 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 itemsto measure the same concept. Cronbach’s Alpha coefficients for the theoretical concepts areprovided 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 internalconsistency with a coefficient of only .46. In further exploratory analysis of the individual

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technology items (results not reported here), none of the items showed any significant orsubstantial exploratory power. Therefore all of these questions and responses were deleted.

Table 2: Technology and Internet Use Experience Comparison for Four ConsumerGroups

χ2InternetInternetWeb-StoreNon-WebShopper

Total(n=266)

CategoryTech.Experience BuyerBrowserVisitor

(n=13) (n=99)(n= 88)(n=66)

23.18*7 (7.7%)12 (13.6%)7 (10.6%)1 (7.7%)27 (10.1%)< 3 yrsComputer21 (21.2%)16 (18.2%)26 (39.4%)4 (30.8%)67 (25.2%)4-6 yrsuse71 (71.7%)60 (68.2%)33 (50.0%)8 (61.5%)172 (64.7%)< 7 yrs

10.9715 (15.2%)17 (19.3%)12 (18.1%)1 (7.7%)45 (16.9%)< 3 yrsInternet84 (84.8%)71 (80.7%)54 (81.8%)12 (92.3%)221 (83.1%)> 7 yrsUse

3.0118 (18.2%)17 (19.3%)14 (21.2%)5 (38.5%)54 (20.3%)Some skillfulAbility to useInternet 81 (81.8%)71 (80.7%)52 (78.8%)8 (61.5%)212 (79.7%)Skillful

5.9199 (100 %)87 (98.9%)63 (95.5%)13 (100%)262 (98.5%)PrivateInternet0 (0.0 %)1 (1.1%)3 (4.5%)0 (0.0%)4 (1.5%)Publicaccess

4.9625 (25.3%)24 (27.3%)21 (31.8%)7 (53.8%)77 (28.9%)Slow (Dial up)Speed of74 (74.7%)64 (72.7%)45 (68.2%)6 (46.2%)189 (71.1%)Fast (DSL,

etc.)Internet

15.52*21 (21.2%)32 (36.4%)30 (45.5%)5 (38.5%)88 (33.1%)< 3 hrsHours of51 (51.5%)38 (43.2%)26 (39.4%)8 (61.5%)123 (46.2%)4-10 hrsInternet use27 (27.3%)18 (20.5%)10 (15.2%)0 (0.0%)55 (20.7%)< 11 hrs

16.8730 (30.3%)19 (21.6%)21 (31.8%)1 (7.7%)71 (26.7%)Search & shopPrimary47 (47.5%)43 (48.9%)37 (56.1%)12 (92.3%)139 (52.3%)Communica-

tionInternet use

22 (22.2%)26 (29.5%)8 (12.1%)0 (0.0%)56 (21.0%)Entertainment

Data displayed as n (%).*p < .05.

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Table 3: Cronbach’s α Values for Hypothesized Model

Cronbach’s α (0<α<1)SubscaleTheoretical Factor

.25PrivacyConsumer Factor

.29Security

.73Time saving

.51Ease of use

.58Convenience

.66Enjoyment

.46Company reputation

.28Previous experience

.17Tactility

.81Consumer Factor Scale Score

.22PriceMarketing Factor

.24Product

.70Promotion

.09Delivery

.04Return

.36Customer service

.65Marketing Factor Scale Score

.09Internet accessTechnology Factor

.18Download time

.43Representation

.46Technology Factor Scale Score

Findings indicate it is possible to measure collectively respondents’ consumer and marketingattitudes as a single factor. This finding offers greater parsimony in model building, thusimproving statistical testing. Not only do the sub-factors hold together as a scale but theyalso 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 marketingarea.

Attitudinal Differences on Internet Shopping for Four Consumer GroupsFor 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 groupsof 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 towardthe use of the Internet as an alternative shopping tool, still all three of the parsimonious attitudescores for the consumer and marketing factors were lower than those of Internet buyers. Thehigher 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 GroupsInternet BuyerInternet BrowserWeb-Store VisitorNon-Web ShopperFactor(n=99)(n=88)(n=66)(n=13)

FSDMeanSDMeanSDMeanSDMean

42.09**9.0c74.710.4b64.28.1ab61.05.7a56.2Consumer Factor Score

13.22**4.4c42.15.1b39.25.2ab38.66.3a34.8Marketing FactorScore

a…c Different superscripts denote significant differences between groups by Tukey’s posthoc analyses.**p < .0001.

Prediction of the Consumers’ Intention to Purchase on the InternetInternet shopping intention was regressed on consumer factors, years of computer use, andInternet access methods. These three predictors accounted for just above half of the variancein 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 showeda 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 factorshowed little predictive ability in this study. This may have been influenced by the weakrelationship identified by the moderate alpha coefficient. The technology sub-factors did nothold together at all as a single factor. This may be related to the study subjects, the vastmajority of whom exhibited high technology use and experience. [See Table 5.]

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Table 5: Prediction of Intention to Purchase Online

pβPredictor.000*.074Consumer Factor Score.543-.010Marketing Factor Score.507-.073Age.834.029Gender.256.205Ethnicity.949.013Marital status.931-.091Income.106-.254Self-support.371-.098Number of credit cards.445-.131Residence.000*.505Years of computer use.104-.375Years of Internet use.870.031Internet use ability.034*1.219Access to Internet.316-.152Speed of the Internet.069.184Hours of Internet use.253-.115Primary usage of Internet

R 2 = .538.F = 8.704.*p < .05.

Conclusions and SuggestionsFindings of the study suggest that Internet retailers should provide convenience, securetransactions, and a complete product description as well as ample visual presentations ofmerchandise. Retailers should also provide an enjoyable atmosphere in order to make Internetshopping advantageous over other retail outlets. Also, successful e-tailers will respond tothe 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 attitudesabout buying products online. Such insights offer e-tailers and business owners suggestionson 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 anonline 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 creatinga site that attracts web-visitors to spend more time. For browsers, privacy and security pro-

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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 providingmore 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 moreonline 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 behaviorliterature in four ways. First, it groups the most frequently cited variables in the literatureinto three parsimonious factors. These factors were then tested, and it was confirmed thatthe consumer factor is most influential. Second, the study confirms that individual attitudesare predictor of intention, supporting the finding of Shim et al.’s (2001) study and goes onestep further by offering that the individual’s intention to purchase online is a predictor ofpurchasing behavior. Finally, the data adds to the literature by providing that consumers canbe 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 thesegroups can be separately distinguished and analyzed as to their profiles and why each hasor has not yet adopted online buying as a behavior.

Finally, the current study has several contributions for consumer educators by providinga framework by which they may apply the Theory of Reasoned Action (TRA) in their ownclasses to other real-life consumer applications. TRA provides a behavioral explanation ofthe importance of attitudes on a prospective buyer’s decision-making process and explainsthe sequence of the human behavioral process from attitude, intention and behavior. Byhaving an example of how to apply TRA, educators can explain to students that there is arational sequential decision making process in virtually all consumer transactions.

Second, consumer educators will better understand the benefit of classifying a subjectresearch group into smaller sub-groups to better serve the needs of the subject group. Forexample, the researcher grouped target consumers into four groups based on their character-istics instead of analyzing the single group of online consumers.

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About the AuthorDr. Jongeun KimJongeun Kim, Ph.D., is an Assistant Professor of Apparel Design and Merchandising atCalifornia State University, Northridge. She received her B.S. in Sociology from EwhaWomen’s University in Seoul, Korea and earned a second B.S. and her M.A. both in ApparelDesign and Merchandising from Kon-Kuk University in Seoul, Korea. She received herPh.D in Human Environmental Sciences from Oklahoma State University in Stillwater. Kimhas been teaching in higher education for over 10 years and has developed courses in appareldesign, fashion theory, the culture and psychology of fashion, special needs/functionalclothing and apparel and textiles in the global economy. Kim’s research focuses on consumerbehavior, e-commerce and m-commerce marketing, sustainability and eco and green fashion.Kim has presented her work at national and international conferences, published her researchin journals and conference proceedings and organized workshops and seminars sponsoredby 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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