This article was downloaded by: [Florida State University], [Eundeok Kim] On: 23 January 2012, At: 09:53 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Fashion Design, Technology and Education Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tfdt20 e-Mass customisation apparel shopping: effects of desire for unique consumer products and perceived risk on purchase intentions Ju-Young M. Kang a & Eundeok Kim a a Department of Design, Housing, and Apparel, University of Minnesota, Saint Paul, USA b Department of Retail Merchandising and Product Development, Florida State University, Tallahassee, USA Available online: 18 Jan 2012 To cite this article: Ju-Young M. Kang & Eundeok Kim (2012): e-Mass customisation apparel shopping: effects of desire for unique consumer products and perceived risk on purchase intentions, International Journal of Fashion Design, Technology and Education, DOI:10.1080/17543266.2011.641593 To link to this article: http://dx.doi.org/10.1080/17543266.2011.641593 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
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This article was downloaded by: [Florida State University], [Eundeok Kim]On: 23 January 2012, At: 09:53Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
International Journal of Fashion Design, Technologyand EducationPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tfdt20
e-Mass customisation apparel shopping: effects ofdesire for unique consumer products and perceivedrisk on purchase intentionsJu-Young M. Kang a & Eundeok Kim aa Department of Design, Housing, and Apparel, University of Minnesota, Saint Paul, USAb Department of Retail Merchandising and Product Development, Florida State University,Tallahassee, USA
Available online: 18 Jan 2012
To cite this article: Ju-Young M. Kang & Eundeok Kim (2012): e-Mass customisation apparel shopping: effects of desire forunique consumer products and perceived risk on purchase intentions, International Journal of Fashion Design, Technology andEducation, DOI:10.1080/17543266.2011.641593
To link to this article: http://dx.doi.org/10.1080/17543266.2011.641593
PLEASE SCROLL DOWN FOR ARTICLE
Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions
This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.
The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses shouldbe independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly inconnection with or arising out of the use of this material.
e-Mass customisation apparel shopping: effects of desire for unique consumer products and
perceived risk on purchase intentions
Ju-Young M. Kanga* and Eundeok Kimb
aDepartment of Design, Housing, and Apparel, University of Minnesota, Saint Paul, USA; bDepartment of Retail Merchandisingand Product Development, Florida State University, Tallahassee, USA
(Received 20 October 2011; final version received 10 November 2011)
To facilitate apparel firms’ development of effective strategies for e-mass customisation of apparel, this studyexamined the indirect effects of desire for unique consumer products and the perceived risk on purchase intentions ofe-customised apparel based on the theory of planned behaviour. An online survey with a mock website forcustomised business wear was used to collect data from 296 college students. Structural and measurement modelswere estimated. Findings showed that interactive function and a quick and convenient co-design process were theimportant attributes of favourable attitudes towards e-customised apparel. Desire for unique consumer productshad an indirect effect on purchase intention through attitude and subjective norm. Perceived risk had an indirecteffect on purchase intention through subjective norm. However, perceived behavioural control was not a significantpredictor of purchase intention of e-customised apparel. Based on these findings, managerial implications wereprovided.
Keywords: e-mass customisation; desire for unique consumer products; perceived risk
1. Introduction
Mass customisation (MC) is defined as ‘the massproduction of individually customized goods andservices’ (Pine 1993, p. 48). Driven by an emphasison niche markets within the global economy and byconsumer demands for a sense of individuality inproduct options, MC contributes to the eventualcombination of custom-made and mass-producedproducts (Apeagyei and Otieno 2007). MC allowsfirms to produce only the items their customers wantas well as provides cost advantages to firms becauseof lower inventory levels, minimised material waste,flexible production, and most of all, customersatisfaction (Pollard et al. 2008). Including consu-mers in the design process allows retailers to respondto the increased individualisation of demand (Frankeand Piller 2003). One variation of MC involves ‘co-design,’ in which the consumer creates an individua-lised product from a variety of options usingcomputer aided design (CAD) technology (Fiore2008).
An increased number of US apparel and footwearfirms, including Lands’ End, Ann Taylor, Reebok, andNike are using MC. Some firms are very successfulwith MC while others, such as Levi Strauss, are not.Nike was one of the first to provide a ‘build your ownshoe’ option on its website and in 2010 they increased
sales 25% over the previous year. More recently,NikeiD, the e-custom design app, brought in morethan $100 million in sales for the first time (Sloan2010). Zazzle.com, one of the leaders in online apparelMC, has 20 million unique visitors per month and hasincreased traffic 1600% and sales 900% in the pastfour years (Sloan 2010).
MC aims to provide unique products with higherquality and lower cost than that of mass-produceditems, closely matching consumers’ preferences fromdesign to delivery (Kamali and Loker 2002). A varietyof products by MC enhanced differentiation fromother customers and their belongings by means of atruly unique product (Michel et al. 2009). However,consumers hesitate to customise apparel because of theperceived product and transaction risks (Cho andFiorito 2009). Customer co-design can result incomplex, risky, and uncertain purchasing (Pilleret al. 2005).
Previous researchers focusing on apparel MC haveidentified (1) consumers’ high interest in and satisfac-tion with co-design involvement and process as onevariation of MC (Kamali and Loker 2002, Ulrich et al.2003, Choy and Loker 2004) and (2) the effect ofproduct-related factors and technology acceptancemodel variables on willingness to pay, namely productinterest, product category, and preference fit (Franke
et al. 2010); product outcome, complexity, andenjoyment (Dellaert and Dabholkar 2009); productsecurity, usefulness, ease of use, and trust (Cho andFiore 2009); and optimum stimulation level andclothing interest factors (Fiore et al. 2004).
Previous researchers have not clearly identifieddeterminants of positive attitude and perceptions ofonline apparel MC. Further, there is a lack ofempirical research examining the role of individualcharacteristics, such as desire for unique consumerproduct (DUCP) and perceived risk, on perceptions ofand attitude towards apparel MC. Halepete et al.(2009) examined the effects of consumer uniqueness,perceived risk, involvement, and body size on inten-tions towards personalisation of fair trade apparel. Weapplied Halepete et al.’s (2009) model in the context ofapparel MC so that DUCP and perceived risk wereselected in our conceptual model. Further, weattempted to approach this issue with the theory ofplanned behaviour (TPB) (Ajzen 1985) as an extensionof the theory of reasoned action (TRA) (Fishbein andAjzen 1975) with the addition of perceived behaviouralcontrol (PBC). Although many researchers modelspecific behaviours using the TRA, our study em-ployed TPB including PBC over TRA because mostconsumer behaviours are subject to obstacles (Pavlouand Fygenson 2006). TPB has also been one of themost influential theories in explaining and predictingbehaviour (Pavlou and Fygenson 2006).
The purpose of our study was to examine (1) howDUCP and perceived risk affect subjective norm, PBC,and attitude, which in turn affect purchase intentionsof apparel MC, and (2) which attributes of thecustomisation process are the most important onfavourable attitude towards apparel MC. The con-tributions of this study are threefold. First, it criticallyexamines DUCP and perceived risk, integrating TPBand Halepete et al.’s model (2009). Second, it tests TPBwith additional variables to explain e-shoppers’ pur-chase intentions of apparel MC, fulfilling an identifiedgap in the literature. Third, on a managerial level, itprovides beneficial insight for retailers to implementapparel MC.
2. Review of literature
2.1. Online apparel customisation
Apparel products have been identified as the mostappropriate product category for online MC (Gold-smith and Freiden 2004). Apparel MC would culmi-nate in a reduction of mass-produced clothing, morespecialty designs for consumers, and an improvementof apparel quality at a lower cost (Kim and Johnson2007). Some apparel firms employ only MC and otherfirms incorporate apparel MC into their mass
production business models (Senanayake and Little2010). Specifically, postproduction customisation is themost common among the apparel firms while fit anddesign customisation is becoming less prevalent nowa-days (Senanayake and Little 2010).
2.2. The theory of planned behaviour
The theoretical framework for our study is based onthe TPB to explain e-shoppers’ purchase intentionstowards customised apparel on a mass-customisedapparel internet shopping site. TPB explains howconsumers can change their behaviour and predictsintentional consumer actions. TRA can appropriatelypredict straightforward voluntary behaviours (Armi-tage and Conner 2001). However, Ajzen (1985)discovered that behaviour appeared not to becompletely voluntary and under control. As anextension of TRA, TPB is employed in the onlineMC context by using three antecedents of behaviour-al intention of e-customised apparel: attitude towardse-customised apparel, subjective norm (i.e. percep-tions of social pressures by others in e-customisingapparel), and perceived behavioural control (i.e.perceptions of ease or difficulty in e-customisingapparel).
2.2.1. Attitude towards behaviour
This is defined as the manner in which a particularbehaviour is evaluated, whether positively or nega-tively (Ajzen 1991). Several researchers have con-firmed that consumers who had more favourableattitudes towards online apparel shopping had great-er intention to buy apparel online (Seock andNorton 2007a, Fogel and Schneider 2010). Cho andFiorito (2009) found that attitude towards theapparel MC website positively affected behaviouralintention towards the use of apparel e-customisation.Based on the research findings, the followinghypothesis was formulated:
H1: Attitude towards e-customised apparel will posi-tively influence purchase intention towards e-custo-mised apparel.
2.2.2. Subjective norm
This refers to a consumer’s perception of socialpressures placed on him or her by others (Ajzen1991). Digital consumers often consult with their peersrather than advertisements for product informationand advice (Fiore 2008). Purchase intentions towardse-customised apparel may be influenced to a greatextent by the opinions of close friends and associates.
Although no study has examined the role of subjectivenorm on purchase intention in the apparel MCcontext, most studies of online apparel shoppingconfirmed the effect of subjective norm. Empiricalevidence has shown that subjective norm (i.e. family,friends, and virtual communities) had a significantinfluence on online apparel purchasing (Kim et al.2009, Fogel and Schneider 2010). Thus, the followinghypothesis was developed:
H2: Subjective norm will positively influence purchaseintention towards e-customised apparel.
2.2.3. Perceived behavioural control
PBC refers to a consumer’s perception of the ease ordifficulty of performing a behaviour by assessingwhether he or she has the necessary resources andopportunities required to perform a behaviour (Ajzen1991). In the context of e-shopping, PBC via an onlinestore (Kim and Park 2005) and perceiving less difficultyor complexity with the use of the internet (Pavlou2003, Swinyard and Smith 2003) had indirect anddirect influence on online purchase intentions. Withregard to online apparel MC, shoppers new to MCmay hesitate to use MC because they are not confidentabout the final customised products (Cho and Fiorito2009). Kamis et al. (2008) found that perceived ease ofuse indirectly influenced intention to purchase forcustomised products online mediated by perceivedusefulness. Other researchers documented that whenconsumers were faced with a complex form of onlineMC, they were likely to perceive less control, and thisreduced their intention to use the MC process for cellphone covers (Dellaert and Dabholkar 2009). Thus,the following hypothesis was formulated:
H3: PBC will positively influence purchase intentiontowards e-customised apparel.
2.3. Desire for unique consumer products (DUCP)
Specific indications of the DUCP encompass ‘anincreased tendency to acquire and use products thatare scarce, innovative, customised, and/or outmodedas well as an increased tendency to shop at small,unique retail outlets’ (Lynn and Harris 1997, p. 604).DUCP is identified as one of the sub-items of need foruniqueness (Armstrong et al. 2009). Need for unique-ness was positively correlated with innovativeness(Workman and Kidd 2000). Individuals who areinnovative are adventurous in demanding new pro-ducts and have favourable attitudes towards newapparel products (Kim and Schrank 1982). Further,MC by the use of computer-facilitated production is
one strategy of providing for consumers’ need foruniqueness (Tian et al. 2001). Previous researchersrevealed that consumers who have a need for self-uniqueness had a more favourable attitude towardspersonalisation of apparel and novel products (Work-man and Kidd 2000, Halepete et al. 2009). Kalyanara-man and Sundar (2006) found that perceived noveltyinfluenced attitude towards customised web portals.Additionally, limited researchers documented a relation-ship between DUCP and PBC. In the context of MC forautomobiles, the perceived complexity of choice wasreduced by the need for uniqueness (Dabic et al. 2008).We reasoned that consumers with high levels of DUCPwould have a favourable attitude towards and perceivedease of use of e-customising apparel. Thus, the followinghypotheses were developed:
H4: DUCP will positively influence attitude towardse-customised apparel.
H5: DUCP will positively influence PBC.
2.4. Perceived risk
Perceived risk refers to a consumer’s subjective beliefof the potential of experiencing loss while seeking adesired outcome (Pavlou 2003). Consumers are reluc-tant to shop for clothing online, as they associate thistype of purchase with a higher risk than they do within-store shopping because they are unable to try ongarments, feel fabrics, and read care labels (Zhou et al.2007, Cho and Fiorito 2009). Previous researchersidentified that perceived risk negatively influencedshopping intentions (Park et al. 2004) and attitudetowards purchasing online (Heijden et al. 2000). Otherresearchers found that perceived security indirectlyinfluenced attitude towards online apparel MCmediated by trust in the apparel MC website (Choand Fiorito 2009). Additionally, some empiricalevidence supported a relationship between perceivedrisk and PBC. Featherman and Pavlou (2003) identi-fied the relationship between perceived ease of use andperceived risk. Similarly, perceived risk originated inseveral antecedents including the consumers’ familiar-ity with the internet (Schoenbachler and Gordon2002). Consumers with high levels of product purchas-ing familiarity may experience low levels of perceivedrisk (Pires et al. 2004). Thus, we reasoned thatconsumers with low perceived risk would have afavourable attitude towards and perceived ease of e-customising apparel. The following hypotheses weredeveloped:
H6: Perceived risk will negatively influence attitudetowards e-customised apparel.
H7: Perceived risk will negatively influence PBC.
International Journal of Fashion Design, Technology and Education 3
A convenience sample of 400 college students in twolarge southeastern US universities was recruited. Atotal of 301 students participated in our main survey(response rate ¼ 75.25%) and were given extra creditas compensation for participating in our survey. Eventhough the use of a college student sample limits thepotential for generalisation of findings to a wider rangeof consumers, members of the college student agegroup have a particular importance as a target marketfor internet-based commerce and are influential con-sumers when it comes to purchasing apparel online(Seock and Norton 2007b).
3.2. Stimulus
To determine apparel categories for our researchwebsite, a pilot study was conducted. Seventy collegestudents (53 women and 17 men) were surveyed witha paper-and-pencil questionnaire. They were askedwhich apparel types they would most like tocustomise online. The results indicated that amongsix different apparel types, male respondents weremost likely to customise casual wear as the topapparel category, followed by business wear, activesportswear, special occasion wear, bridal wear, andintimate wear. Female respondents were most likelyto customise casual wear, followed by specialoccasion wear, business wear, active sportswear,bridal wear, and intimate apparel. Although it wasnot the first choice of either group, e-customisedbusiness wear was selected for our study because (1)business wear was relatively highly ranked by bothgroups and (2) there is a lack of empirical datarelated to MC for business wear. Most researchersfocusing on apparel MC used casual wear such as t-shirts and jeans (Ulrich et al. 2003, Cho and Fiorito2009, Dellaert and Daboholkar 2009, Franke et al.2010), children’s wear (Lee 2004), and wedding gowns(Choy and Loker 2005) as stimuli. Most US apparelMC websites also focus on casual wear. Dependingon a product category of a stimulus, the results mayvary.
Our mock website (see Figure 1) allowedrespondents to try customising women’s (i.e. jacket,shirt, pants, skirt) and men’s (i.e. jacket, shirt, pants)business wear. Our mock website offered (1) custo-mised garment component options (i.e. choices ofsilhouette, collar, pocket, hem, sleeve, length, pleat,colour, and fabric), (2) customised bodymeasurements and size options (i.e. body shape,height, weight, build, bust, shoulder, sleeve length,neck size, and size for each business wear
component), and (3) customised service options (i.e.availability of a consultant, delivery of actual fabricsamples, and information regarding the latest fashiontrends).
3.3. Procedure
A pretest was conducted with 10 college students to getrespondents’ comments regarding directions of thestimulus contents and questions. For our main survey,instructors at the participating universities created aListserv1 and sent a recruiting email including theURL of the questionnaire to a total of 400 of theirstudents. Respondents first browsed a research websiteas much as they wanted then they were asked to answerthe questions online. All measures and procedureswere approved by a southeastern US university reviewboard.
3.4. Measures and measurement model evaluation
The questionnaire consisted of six measures. Allmeasurement items, reported reliabilities, responseformats, and measurement origins are shown in Table1. Purchase intention was operationalised by thelikelihood of purchasing e-customised wear in thenear future. Attitude encompassed nine general attri-butes representing attributes of the e-customisationprocess. The overall attitude score was derived as thesummation of the products of both the score of beliefs(bi) and the score of the evaluation of those beliefs (ei);that is, A ¼ Sbiei. The overall subjective norm scorewas also derived as the summation of the products ofboth the score of the respondents’ normative beliefs(ni) and the score of their motivation to comply withthose referents (mi) for all the referents; that is,SN ¼ Snimi. Further, the direct measure items ofPBC were revised to measure perceptions of ease ordifficulty in e-customising apparel. Ajzen (2002)reported that ‘it is possible to obtain high reliabilitieswith direct measures of PBC’ (p. 671). Severalresearchers measured PBC through the standard directapproach (e.g. Conner and McMillan 1999, Shimet al. 2001). Respondents were also asked to supplydemographic information about themselves, includ-ing age, sex, and ethnicity, and online shoppingexperience.
Confirmatory factor analysis indicated thatthe measurement model had acceptable constructvalidity and reliability. Table 2 provides an over-view of construct means, standard deviations, AVE,the composite reliability, R2, and correlations forthe measurement model. The overall fit statistics(w2 ¼ 652.08 with 382 df, w2/df ¼ 1.71, CFI ¼ .95,NNFI ¼ .94, IFI ¼ .95, RMSEA ¼ .049, and
SRMR ¼ .055) suggested that the measurementmodel was a good fit. Convergent validity wassupported by the following: (1) all loadings for itemswere significant (p 5 .001), (2) the composite relia-bility for each construct exceeded the recommendedlevel of .70, and (3) the average variance extracted(AVE) for each construct fulfilled the recommendedbenchmark of .50 (Hair et al. 1988). As evidence of
discriminate validity of the scales, none of theconfidence intervals of the phi estimates included 1.00.
4. Results
4.1. Participant characteristics
Out of 301 responses collected, 296 were usable andwere employed for our data analyses. There were 128
Figure 1. Apparel mass customisation research website used for the study: (a) Page 1: Opening page (top), (b) Page 4: Men’sjacket (bottom).Note. Our mock website contained twelve pages: a welcome page, a definition of MC and co-design, a consent form, nine pagesfor women’s and men’s business wear, and a screen on which to enter body measurements.
International Journal of Fashion Design, Technology and Education 5
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males (43.24%) and 168 females (56.76%) respon-dents. The age range was from 17 to 46, with a meanage of 21.16 years. Respondents were pursuingundergraduate (81.4%) or graduate (17.9%) degrees.Most of the respondents were White/Caucasian
(62.8%), followed by Hispanic/Latino (12.5%) andBlack/African-American (11.8%). In terms of fre-quency of respondents’ online shopping experience,‘once every three months or less’ had the highestoverall percentage of respondents (42.6%) followed
Figure 1. Continued. Apparel mass customisation research website used for the study: (c) Page 9: Women’s shirt (top), (d) Page11: Women’s pants (bottom).Note. Male respondents had 108 choices for the jacket, 162 choices for the shirt, and 162 choices for the pants; femalerespondents had 216 choices for the jacket, 108 choices for the shirt, 243 choices for the pants, and 243 choices for the skirt. Thecomputer illustration showing their co-designed garments changed when respondents adjusted their selections.
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Table 1. Constructs, indicators, and key statistics.
I will try to purchase customised apparel in the near future* .94b
I plan to purchase customised apparel in the near future* .90a(15.06)Cronbach a ¼ .92
Attitudec
(Ajzen 1991)A ¼ S biei
A variety of design choices* .76b
Co-design provides a variety of unique style choices. b1A variety of style choices is important in the co-design process. e1A variety of fabric and colour choices* .70a(16.08)Co-design provides a variety of fabric and colour choices. b2A variety of fabric and colour choices is important in the co-design
process. e2Perceived usefulness* .80a(14.02)Mass customisation provides perceived usefulness. b3The usefulness of co-design is important in the co-design
process. e3Enjoyment* .82a(14.32)Co-design provides enjoyment. b4Enjoyment of the co-design process is important. e4A better fit* .77a(13.28)Mass customisation provides a better fit. b5Availability of a better-fitting garment is an important benefit of a
mass-customised product. e5The availability of a consultant* .67a(11.39)The availability of a consultant in a mass-customised apparel internet site is
helpful. b6Availability of a consultant is important in the co-design process. e6Interactive functions* .85a(14.57)A mass-customised apparel Internet site has interactive functions. b7Interactive functions are important in the co-design process. e7A quick and convenient process* .80a(13.62)Mass customisation provides a quick and convenient co-design process. b8Speed and convenience are important in the co-design process. e8Virtual reality* .74a(12.68)A mass-customised apparel website provides an experience of virtual
reality. b9Virtual reality features are important in the co-design process. e9
Cronbach a ¼ .93Subjective normd
(Ajzen 1991)SN ¼ S nimi
Close friends* .93a(15.42)My close friends are likely to think that it would be good for me to co-
design/customise garments on a mass-customised apparel website. n1My close friends influence my decision to co-design. m1
Other important people* .89b
Other important people around me are likely to think that it would be goodfor me to co-design/customise garments on a mass-customised apparelwebsite. n2
Other important people around me influence my decision to co-design. m2
Cronbach a ¼ .82PBC (Ajzen 1991) I am confident that if I wanted to, I could co-design apparel products* .81a(13.67)
I believe that I have a lot of control over the process of customisingproducts, because of my ability to use the internet*
.75a(13.03)
The apparel co-design process is easy for me* .79b
Cronbach a ¼ .83DUCP (Lynn andHarris 1997)
I am very attracted to rare objects# .65a(10.65)I tend to be a fashion leader rather than a fashion follower# .66a(10.76)I am more likely to buy a product if it is scarce# .74a(12.06)I would prefer to have products custom-made rather than ready-made# .56a(8.63)I enjoy having things that others do not# .67a(10.82)I rarely pass up the opportunity to order custom features on the products I
buy#.52a(7.88)
I like to try new products and services before others do# .64a(10.40)I enjoy shopping at stores that carry merchandise that is different and
unusual#.74b
Cronbach a ¼ .86
(continued)
International Journal of Fashion Design, Technology and Education 7
by ‘once a month or less’ (25.0%). Most of therespondents (68.6%) had experience with a mass-customising website.
4.2. Attributes influencing attitude
The nine attributes of consumers’ attitudes towardse-customised apparel were examined (see Table 3).The most important attribute for e-customisedapparel was interactive functions (M ¼ 29.31), fol-lowed by a quick and convenient co-design process(M ¼ 28.44) and availability of a consultant(M ¼ 27.14).
4.3. Gender differences
In order to examine whether males and females differon all research variables, an independent samples t-testwas used. Men did not differ from women with regardto all variables (p 4 .05) except for perceived risk. The
average perceived risk for women (M ¼ 3.91) washigher than the average perceived risk for men(M ¼ 3.64), t(278) ¼ 72.061, p 5 .05. Further, mul-tiple group structural equation modeling (SEM)analysis was conducted to compare gender groups ina hypothesised model. The model fit difference fromthe comparison of the two groups indicated that thecoefficient for the male model and the female modeldid not differ significantly (Dw2 ¼ 3.098, Ddf ¼ 7,p ¼ .87; Base model: w2 ¼ 1265.56 with 776 df; Modelwith equality constraint imposed: w2 ¼ 1268.66 with783 df).
4.4. Model testing
A structural analysis was conducted using the max-imum likelihood estimation method. The structuralmodel exhibited a good fit with the data (w2 ¼ 746.58with 389 df, w2/df ¼ 1.92, CFI ¼ .93, NNFI ¼ .92,IFI ¼ .93, RMSEA ¼ .056, and SRMR ¼ .073). H1,
In general, purchasing a customised garment on the mass-customisedapparel website is risky
.69a(8.20)
In general, purchasing any product through a mass-customised apparelwebsite is risky
.71a(8.58)
The customised garment will end up not being as good as what I expected .78a(11.07)Purchasing the customised garment on the mass-customised apparel
website will end up wasting my time.70a(10.80)
The customised garment on the mass-customised apparel website will notfit properly
.79a(11.67)
The customised garment on the mass-customised apparel website will notaccurately express my self-image
.60b
Cronbach a ¼ .86
Note: #Strongly disagree (1) and strongly agree (5). *Strongly disagree (1) and strongly agree (7). aFactor significance: p 5 .001; bLoading was setto 1.0 to fix construct variance. cAttitude toward the behaviour (A) is a function of two components: (1) beliefs (bi) that performing a behaviourhas certain attributes; and (2) the evaluation of those beliefs (ei): A ¼ S biei.
dThe subjective norm is a function of two determinants: (1) theindividual’s normative beliefs (ni), which indicate that specific individuals or groups think that the individual should or should not perform thebehaviour, and (2) the individual’s motivation to comply with those referents (mi): SN ¼ S nimi.
(A ! PI: b ¼ .31, t ¼ 4.87, p 5 .001), H2(SN ! PI:g ¼ .32, t ¼ 5.19, p 5 .001), H4(DUCP! A: g ¼ .34,t ¼ 5.33, p 5 .001), H5(DUCP ! PBC: g ¼ .53,t ¼ 7.43, p 5 .001), H6(PR ! A: g ¼ 7.38,t ¼ 75.72, p 5 .001), H7(PR ! PBC: g ¼ 7.24,t ¼ 73.76, p 5 .001) were supported. However, H3
(PBC ! PI: b ¼ .07, t ¼ 1.01, p 4 .05) was notsupported. To improve our proposed model, thesignificance of the regression weights was examinedfirst. Modification indices were then used to identifyany theoretically meaningful paths/relationshipsomitted and added to our original model. The revisedmodel was tested (see Figure 2). One importantrelationship was identified. This relationship wasbetween DUCP and subjective norm (g ¼ .49,t ¼ 7.16, p 5 .001). Our revised model was found tofit the data better than our proposed model,w2 ¼ 538.94 with 311 df, w2/df ¼ 1.73, CFI ¼ .95,NNFI ¼ .94, IFI ¼ .95, RMSEA ¼ .050, andSRMR ¼ .059.
5. Discussion
Our results not only verify the indirect effects of DUCPand perceived risk on purchase intentions of e-customised apparel mediated by attitude, subjectivenorm, and PBC but also provide insights into theimplementation and development of apparel MC. Ourresults reveal that DUCP exerted a significant influenceon attitude, subjective norm, and PBC. In other words,e-shoppers who are very attracted to rare products andwho enjoy shopping for unusual products that othersdo not have were likely to have a favourable attitude,perceived ease of e-customising apparel, and per-ception of social pressure from others regarding
e-customising apparel. This is consistent with thedocumented relationships between the need foruniqueness and attitude as well as PBC (Dabic et al.2008, Halepete et al. 2009). Our study also revealedthat e-shoppers with DUCP were likely to have higherpurchase intention towards e-customised apparelthrough their positive attitude and subjective norm.Thus, the target market of apparel MC could be basedon e-shoppers who seek unique products.
Our study identified that e-shoppers who had highperceived risk were likely to have a negative attitudetowards and to perceive difficulty of e-customisingapparel. Further, perceived risk had an indirect effecton purchase intention mediated by attitude, agreeingwith the findings of Heijden et al. (2000). We also foundthat women were more likely to have a higher perceivedrisk than men. Our findings suggest that retailers shoulddevise strategies to reduce users’ perceived risk andovercome uncertainty about finished customised pro-ducts and purchase. Specifically, retailers could provide3D virtual try-on technologies, a live chat system with aco-design consultant, delivery service of actual fabricsamples for options selected by users, security policiesregarding personal information, and return policiesguaranteed by repair, refund, or exchange.
Attitude and subjective norm exerted a significantinfluence on purchase intention of e-customisedapparel, supporting TRA. Among nine attributes offavourable attitudes towards e-customised apparel,interactive functions of website as well as a quickand convenient co-design process were the mostimportant attributes. A variety of style, colour, andfabric choices was the most important attributeinfluencing evaluations of e-customised apparel, con-sistent with previous studies (Kamali and Loker 2002,Ulrich et al. 2003). However, excess variety may leadto an external complexity of MC (Franke and Piller2003). Our findings suggest that retailers shouldenhance the quality of apparel MC websites byfacilitating quick and easy transactions as well as ahigh level of image interactivity. Further, firms shoulddevelop an appropriate design toolkit for apparel MCto fulfil e-shoppers’ demand for individualised designchoices. In addition to attitude, purchase intentions ofour participants were influenced to a great extent bythe recommendation, opinion, or references given bytheir close friends and the important people aroundthem (i.e. subjective norm). Trust in recommendationsis generally stronger when the recommendations comefrom peers and when those ratings affect the reputationof the recommending party (Boyd 2002). This leads usto suggest that virtual (online) communities in whichusers can interact with each other can facilitate theproduction of reliable recommendations (Schubert andGinsburg 2000).
Table 3. Respondents’ beliefs about and evaluations ofattributes regarding e-customised apparel.
Attribute biei M bi M ei M
1. Interactive functions 29.31 5.20 5.462. A quick and convenient
co-design process28.44 5.07 5.47
3. A better fitting garment 27.14 4.70 5.604. Availability of a consultant 26.16 4.97 5.045. A variety of unique style
choices25.91 4.52 5.63
6. Enjoyment of the co-designprocess
25.83 4.79 5.23
7. Experience of virtual reality 25.13 4.67 5.278. Perceived usefulness of the
co-design process25.02 4.68 5.23
9. A variety of colour andfabric choices
22.51 3.89 5.78
Note: Attitude toward the behaviour (A) is a function of twocomponents: (1) beliefs (bi) that performing a behaviour has certainattributes; and (2) the evaluation of those beliefs (ei): A ¼ S biei.
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Contrary to our hypothesis, PBC did not influencepurchase intention towards e-customised apparel. Thiscan be explained by the fact that the homogeneousnature of the sample may have caused a lack ofimportance of PBC. The college student age group isvery familiar with the use of the internet as well asusually searches for and purchases products via theinternet as a powerful means (Seock and Norton2007b). Due to their ability to use the internet as wellas familiarity and involvement with e-shopping, collegestudents were likely to perceive very little difficulty in e-customising apparel. As a result, although ourproposed model was based on the TPB includingPBC, we found TRA is more applicable than TPB inthe context of online apparel MC.
6. Implications
The implications and insight offered by our studycould be utilised by retailers as a foundation todevelop efficient strategies for online apparel MC.We recommend marketing strategies focusing onfulfilment of e-shoppers’ desire for unique products.In that sense, style, colour, and fabric choice andservice, website layouts, and advertising or promo-tions should satisfy users’ increased tendency to seekout uniqueness. Specifically, a design toolkit in theMC process should include unique design, style,detail, and fabric MC options through the applica-tion of technology (e.g. Made-to-Measure program,CAD, etc.).
Figure 2. Proposed model and revised model. Note. Proposed model: w2 ¼ 746.58 with 389 df, w2/df ¼ 1.92, CFI ¼ .93,NNFI ¼ .92, IFI ¼ .93, RMSEA ¼ .056, and SRMR ¼ .073. Revised model: w2 ¼ 538.94 with 311 df, w2/df ¼ 1.73,CFI ¼ .95, NNFI ¼ .94, IFI ¼ .95, RMSEA ¼ .050, and SRMR ¼ .059 (***Path significance: p 5 .001; n.s. not significant:p 4 .05).
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Our findings about the importance of subjectivenorm suggest that retailers actively conduct a fashion2.0 community or online community for co-design onapparel MC websites for C2C interactions. Forexample, the apparel MC website Zazzle.com, includ-ing Web 2.0 capabilities, creates a community withforums for sharing experiences, a blog for commentson designs, and fostering aesthetic creativity forcustomisation. Thus, these communities of co-designenable users to interact with each other and couldfurther increase trust and reduce perceived risk (Pilleret al. 2005). In addition to co-design communities,retailers should actively use social networking sites,YouTube, and even mobile channels to manageexpanded marketing communications. As an extensionof e-commerce, mobile commerce allows e-shoppersaccess at any time and in any place, so retailers shouldlaunch m-commerce MC websites to increase usage ofand access to apparel MC websites.
Additionally, 3D virtual try-on technologies shouldbe actively implemented on apparel MC websites toreduce the perceived risk of e-customising apparel.These virtual try-on technologies are now available onthe H&M fashion studio and My Virtual Modelwebsite that supports est.Today.com. However, thesetechnologies are not yet widely available in e-commercebecause developing virtual clothing that drapes andmoves with the body like actual fabric has continued tobe challenging for retailers (Loker et al. 2008, Lim et al.2009). The investigation of this technology using thefabric drape software goes on (Lim et al. 2009). In thenear future, consumers will be able to store their ownbody scans on a smart card or with their mobile phonesthen use the virtual fit applications on apparel MCwebsites (Lim et al. 2009), which may reduce un-certainty about e-customised apparel fit issues.
Marketers should also develop service-based stra-tegies with special events, promotions, and incentives.Specifically, a live chat with a consultant on theapparel MC website may reduce product risk andincrease users’ trust and loyalty. Dellaert and Dab-holkar (2009) noted that e-salesperson interactionoffers useful feedback and direct responses that canbe used to reduce the uncertainty that users face in theco-design process. Furthermore, providing co-designcontests or fashion shows and incentives for frequentusers and helping users market their own designsshould be considered in order to expand for new usersand generate more profit.
7. Limitations and suggestions for future study
Generalisation of the findings is limited due to the useof a convenience sample of college students, who havea higher rate of internet use than other population
groups. Future results may differ in accordance withother consumers’ ages, occupation, and income; there-fore, using a representative sample of consumers withvarying demographic and psychographic factors ishighly recommended. In addition, our mock websiteprovided respondents with an opportunity to custo-mise business wear with highly interactive functionsbut offered limited design choices and virtual reality.Findings can also generate different results accordingto different apparel category of a stimulus. Finally, ourconceptual model did not include control variables ormoderating variables related to e-shoppers’ fashioninvolvement, aesthetic appreciation, price or qualityconsciousness. The examination of the moderatingeffect of those variables should be extended in futureresearch.
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