A flow-based model of web site intentions when userscustomize products in business-to-consumerelectronic commerce

Arnold Kamis & Tziporah Stern & Daniel M. Ladik

# Springer Science + Business Media, LLC 2008

Abstract User-customization is increasingly common inelectronic commerce, because both the buyer and sellerpotentially benefit. The user interface to implement and theinfluence of the interface on various process and outcomemeasures, however, are not well understood. We developed aFlow-based model consisting of seven hypotheses regardingthe user interface and its consequents. We conducted a fieldexperiment to test an attribute-based interface vs. a question-based interface on three variables (perceived control,shopping enjoyment and choice satisfaction) as well as twoweb site intentions: intention to return and intention topurchase. Six of the seven hypotheses were supported in a

parsimonious model. Variance explained was 16.3% forperceived control, 45.6% for shopping enjoyment, 59.3% forchoice satisfaction and 63.1% for web site intentions. Themain finding is that an attribute-based interface for retail e-shopping increases the shopper’s sense of control and feelingof enjoyment in the process more than a question-basedinterface, and thereby increases satisfaction with the out-come. This combination of influences increases the intentionof the shopper to return to the web site and to purchase theitem. We discuss the results and suggest areas for futureresearch in user-customization, which may apply to manydifferent industries that engage in online commerce.

Keywords Business-to-consumer electronic commerce .

Flow-based model . Users

1 Introduction

Online customers are not just attentive spectators, they areoften active players as well, supplying labor andknowledge to the service creation process (Bowen 1986).

The customer participation literature has developed the roleof the consumer as a co-creator or co-producer of products orservices (Kelley and Skinner 1990). Customer participation is“the degree of consumer’s effort, both mental and physical,necessary to participate in production and delivery of ser-vice” (Silpakit and Fiskin 1985). Due to the important rolecustomers can play, research has recommended that orga-nizations view customers as partial employees, extending theboundaries of the organization to include customers astemporary members of the firm (Lovelock and Young1979; Mills et al. 1983; Mills and Morris 1986). By self-serving, customers benefit by getting exactly what they want

Inf Syst FrontDOI 10.1007/s10796-008-9135-y

Note: Yogesh K. Dwivedi, Michael D. Williams and ViswanathVenkatesh were the guest editors accepting the article as part of thespecial issue on Adoption and Use of Information & CommunicationTechnologies (ICT) in the Residential/Household Context (seeDwivedi et al. 2008 for editorial).

A. Kamis (*)Department of Information Systems & Operations Management,Sawyer Business School, Suffolk University,8 Ashburton Place,Boston, MA 02108-2770, USAe-mail: akamis@suffolk.edu

T. SternZicklin School of Business, Baruch College,The City University of New York,55 Lexington Avenue at East 24th Street,New York, NY 10010, USAe-mail: tzipk@yahoo.com

D. M. LadikDepartment of Marketing, Sawyer Business School,Suffolk University,8 Ashburton Place,Boston, MA 02108-2770, USAe-mail: dladik@suffolk.edu

in a process under their control, and the organization benefitsby selling at a lower cost or a premium price. By offeringuser-led self-customization, businesses may increase searchefficiency (Diehl et al. 2003), reduce the user’s cognitiveeffort (Nielsen 2000) and improve decision quality (Häubland Trifts 2000), thus improving the likelihood of actualpurchase (Moe and Fader 2004).

An important element of user-led customization is theelicitation of customer needs (Wind and Rangaswamy 2001;Zipkin 2001). It involves both the discovery by customersof their own needs as well as their communication of thoseneeds to the company. Therefore, a critical question foronline companies is which type of interface will be moreappropriate for the needs elicitation process. The right userinterface may result in increased sales and customer loyalty(Berman 2002), whereas an inappropriate user interfacecould result in frustrated customers and lost sales.

In this study, we deploy Flow as a theoretical lens toexamine a nomological network in the online shoppingcontext (Agarwal and Karahanna 2000; Koufaris 2002;Thatcher and Perrewe 2002; Pennington et al. 2003-2004).Flow is the phenomenon of deep engagement or immersionin a focused task, balancing challenges and skills whilehaving a sense of control and losing self- and time-awareness (Csikszentmihalyi 1975; Csikszentmihalyi 1977;Csikszentmihalyi and Csikszentmihalyi 1988). The Flowliterature has been used to describe and model the individualexperience in IT use (Agarwal and Karahanna 2000), e-commerce (Novak 2000; Koufaris 2002) and interactiveentertainment (Polaine 2005). The voluntary online shoppingcontext is quite different from the mandatory, organizationalcontext in which the technology acceptance literature hasbecome mature (Davis 1989; Venkatesh et al. 2003;Venkatesh and Agarwal 2006). In this study, we develop aFlow-based model which augments traditional User InterfaceDesign (UID) research by including three user-focusedvariables: perceived control, shopping enjoyment and choicesatisfaction. That is, our model focuses on the user’sperceptions of different UIDs for decision support in theonline shopping context.

We tested two types of user interfaces: an Attribute-BasedUID and a Question-Based UID. For an example of anAttribute-Based UID, consider the Dell configurator, whichenables users to self-customize computers, printers, tele-visions and other goods by supporting component-by-component substitutions. For examples of Question-BasedUID, consider travel recommender systems, software require-ments engineering or medical expert systems. Both Attribute-Based UID and Question-Based UID can be used for user-ledcustomization and product personalization, a growing trend inelectronic commerce (Pine 1993; Tu et al. 2004).

An Attribute-Based UID is one in which the customer ispresented with a list of attributes and the different values for

each attribute. The customer then chooses a combination ofthe different values and is presented with a view of thecustomized product. Customers can iteratively choose differ-ent values and experiment until they feel they have developedan acceptable product. This has been an established form ofcustomization for many products including sneakers, back-packs, watches, breakfast cereals and computers. In this typeof customizing experience, the customer can interact with thesystem as well as see the results of his/her efforts immediately.

The essence of Question-Based UID is a natural languagedialogue between the system and the user to facilitatesearching the solution space in an intuitive way and toultimately recommend a product that will suit the customer’sneeds. Hence, in essence, a Question-Based UID is arecommender system. Research has demonstrated the efficacyof recommender systems, particularly in e-commerce wherethey can help personalize the shopping experience andincrease sales (Schafer et al. 2001). Ricci and Werthner(2006–2007) define recommender systems as “intelligentapplications that assist users in a decision-making processwhen they do not have sufficient personal experience tochoose one item from an overwhelming set of alternativeproducts or services” (p. 5). A classical recommendationsystem asks users about their preferences upfront and thenconstructs a query based on their responses to retrieveproducts that match the user’s taste. It uses knowledge aboutthe products and consumers’ past buying behavior anddemographics to recommend products the customer mightlike in order to help reduce the complexity of choosing aproduct (Schafer et al. 2001; Ricci and Werthner 2006–2007). This type of system can be helpful in situations wherethere is an overwhelming number of options and the usermay find it difficult to pinpoint which combination ofattributes suits him or her. For example, one way recom-mender systems are used on the Web is to suggest movies ormusic to a customer based on a series of questions or ratings,e.g., Netflix. Question-Based UIDs can be used to recom-mend products that are difficult to represent on the web, suchas fragrance, e.g. Urbanbotanic.com and Gurl.com. In 1999,Proctor and Gamble’s Reflect.com was the first web-site tooffer customized cosmetics based on customers’ response topersonality, taste and feelings. Finally, Zafu.com recom-mends jeans based on customers’ answers to a series ofquestions about styles and fit. Therefore, the researchquestion is: How does a naturalistic, intuitive UID, theQuestion-Based UID, compare to a sophisticated, interactiveuser interface, the Attribute-Based UID?

In the next section, we introduce the research model,present the study’s variables, and develop our hypotheses.We follow with the description of the research design andmethods, data analysis, a discussion of the results, ourstudy’s contributions and limitations, and possibilities forfuture research.

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2 Research model and hypotheses

This study integrates research in Information Systems,Human-Computer Interaction and Consumer Behavior toexamine the effects of Attribute-Based UID vs. Question-Based UID based on Flow (Detlor et al. 2003; Johnson andWiles 2003; Jiang and Benbasat 2005; Qiu and Benbasat2005). Detlor et al. (2003) investigated the inducement ofFlow based on the different informational needs ofsearching vs. browsing. Johnson and Wiles (2003) tookthe domain of computer games as one conducive tounderstanding Flow, so that one can improve affectivedesign in other domains. Jiang and Benbasat (2005) tested auser interface in terms of virtual and functional control withthe aim of increasing the likelihood of Flow. Qiu andBenbasat (2005) studied different user interfaces, specifi-cally text-to-speech and three-dimensional avatars, asmeans of enhancing Flow. An important missing piece inthis stream of literature, however, is the examination of therelationships among process and outcome variables, whileusing different UIDs, which influence two web siteintentions: intention to return and intention to purchase.

Studies of preferential choice tasks, such as this one, allowusers to create a product that satisfies their needs or desires byprocessing multiple types of information (Payne 1976; Toddand Benbasat 1992; Zigurs and Buckland 1998). Thedecision-making process can have multiple alternativesolutions, which the users may compare and evaluate beforeselecting the most desirable one. Based on Flow, we expectthat a technology which is more interactive and engagingwill result in a more strongly preferred, i.e., more desirable,outcome. Research has found that customers who use anAttribute-Based UID should feel that they have all theavailable information for them to make a decision, therebyincreasing their feeling of control (Kahn 1998). One reasonis that with an Attribute-Based UID, users can see theproducts’ attributes, e.g., the watch face design, or watch-band types and their values, e.g. leather or stainless steel,immediately. Hence, they can clearly see what the customiz-able elements of the product are, choose the customizationthey want and see the results interactively. In contrast, with aQuestion-Based UID, the system first requires the user toanswer a series of questions, e.g., about style preferences,and then recommends a choice of customized products fromwhich the users could only infer the customizable attributesalong with the different possible values for each one.

The experience of Flow often occurs in the presence ofimportant facilitators such as perceived control, personalenjoyment, and satisfaction. The first of these, perceivedcontrol, is widely accepted as a human driving force and hasoften been defined as the need to demonstrate one’scompetence and mastery over the environment (White1959). In dynamic environments where the user is guided

by self-motivated goals, Bandura maintains that performanceon a task varies as a function of the individual’s perceivedcontrol regarding that task (Bandura 1982; Bandura 1986).When people perceive that they have a high degree ofcontrol, they exert cognitive effort and persist in the face offailures and setbacks; they evince interest, optimism andsustained attention while problem solving. When peopleexperience a loss of control, they withdraw, retreat, escape,or otherwise become passive. In this sense, people will seekout enjoyable situations they feel they are capable ofmanaging and avoid uncomfortable situations they feel arebeyond their abilities. This pattern of reactions forms thecornerstone for all major theories of perceived control.

Perceived control in the e-commerce context is defined asthe degree to which customers or service providers perceivethat they are able to influence the process and outcome. Withmore control, customers feel more responsibility for, andsatisfaction with, the process and outcomes. In addition,perceived control may be more pertinent in a self-servicesetting in the absence of any front-line service personnel, as theconsumer must perform the entire service for him- or her-self.Service encounter research considers perceived control a crucialelement of e-service quality (Zeithaml et al. 2002). Thus,

H1 Perceived control is greater for an Attribute-BasedUID than for a Question-Based UID.

Many different user interfaces have been developed tosupport the e-shopper in making choices, including example-based (Pu and Kumar 2004), list-based and matrix-basedrepresentations (Häubl and Trifts 2000; Hong et al. 2005)and recommender systems (Ziegler and Golbeck 2007). Thetype of interface, e.g., three dimensional representations ofproducts or sales agents such as avatars can increase choicesatisfaction (Holzwarth et al. 2006; Yen and Ng 2007).Interface designs vary from search engines, which focus onprice-comparison for homogenous products (Bakos 1997;Brynjolfsson and Smith 2000), to user-supporting tech-niques, which offer greater user-control and decisiontransparency for comparing heterogeneous products (Puand Chen 2005; Wujin et al. 2005). For homogeneousproducts, a simple list of alternatives may be an appropriatedesign, because price may be the only differentiator. Forheterogeneous products, however, the interface may have tobe richer to convey several attributes simultaneously or toprovide the user support in examining attribute tradeoffs(Lingyun and Benbasat 2005; Jahng et al. 2006). Someresearch has divided the choice making process intomultiple, sequential stages, supporting each one differently(Gilbride and Allenby 2004; Gilbride and Allenby 2006;Kamis 2006). The success of product configurators and user-led customization demonstrates that the sequential stages canbe many and brief (Pine 1993; Wan 2000). Overall, researchshows that users benefit from UIDs which describe

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alternatives according to meaningful attributes that can beeasily narrowed to the ones most relevant to the user. AnAttribute-Based UID makes the different customizableattributes clear to the user, whereas the process is moreopaque with a Question-Based UID. Thus,

H2 Choice Satisfaction is greater for an Attribute-BasedUID than for a Question-Based UID.

Since users are boundedly rational, they tend to satisficerather than optimize when making decisions (Simon 1972;Simon 1982). They need control over the process to feelable to assess progress to a satisfactory outcome, afterwhich they will likely stop. They do not typically want toexamine every piece of information and every possiblechoice, achieving the optimal choice, because they wouldeither feel information overload or it would simply take toomuch time and effort, i.e., not be worth it. To aid progress,they may choose to use some kind of recommendationagent, e.g., by responding to a Question-Based UID (Wangand Benbasat 2005; Bo and Benbasat 2007), which ispresumed to ask appropriate questions. Alternatively, anenjoyable, Attribute-Based UID requires only incrementaleffort for each iteration, and offers the user a great deal ofcontrol. After each iteration, the user is able to reflect uponthe current choice, decide how satisfied he or she is, assesshow much control he has to improve the outcome, anddecide whether to continue (Urbany 1986) or whether todefer choice, i.e., choose not to choose (Dhar 1996), orwhether to terminate the process with a satisfactory overalloutcome, i.e., the choice. Thus, regardless of the UID,

H3 Perceived control is positively related to choicesatisfaction.

Shopping enjoyment, also based on Flow, has been usedas a hedonic measure of the individual experience whenusing information technology (Agarwal and Karahanna2000), in web environments (Koufaris 2002) and in games(Sweetser and Wyeth 2005; Ryan et al. 2006). Shoppingenjoyment is defined as the intrinsic enjoyment of theinteraction with the web site and it is an important predictorof technology acceptance in Business-to-Consumer e-commerce (Koufaris et al. 2001–2002). Web shopping thatis game-like will be one in which the user feels in control,losing self- and time-awareness, and according to the Flowliterature, experiencing commensurate enjoyment. Thus,regardless of the UID,

H4 Perceived control is positively related to shoppingenjoyment.

According to the Flow literature, enjoyment of theprocess influences the user’s perceptions, subsequentevaluations of the outcomes and the overall technologyused to produce them (Hoffman and Novak 1996).

Research which examines both utilitarian and hedonicfactors within the context of e-service quality shows thatprocess enjoyment has a significant positive impact onchoice satisfaction and customer lifetime value (Bauer et al.2006). Other e-commerce research shows that shoppingenjoyment is a key component of socio-psychologicalvalue, which is itself a predictor of choice satisfaction(Lee et al. 2003). Research on recommender systems hasshown that enjoyment influences the subsequent evaluationof the perceived fit of the recommendation, i.e., the choicesatisfaction (Gretzel and Fesenmaier 2006). Some e-commerce research which separates shopping enjoymentfrom process value found that the process value contributesto choice satisfaction, but that shopping enjoyment does not(Cai and Xu 2006). We argue that enjoyment is a keycomponent of the process, and therefore should not beconsidered in isolation. An enjoyable process confers apositive feeling to whatever the process produces. Thus,regardless of the UID,

H5 Shopping enjoyment is positively related to choicesatisfaction.

The online shopper uses technology not for its own sake,but for a specific purpose, to make a purchase. The likelihoodof purchase, defined as the propensity or probability ofmaking an actual purchase, may vary depending on urgencyof the desire to transact, the helpfulness of the technologysupport, the extent of the user’s product knowledge and otherfactors. Assuming the desire to purchase is present, thetechnology is helpful rather than obstructive, and the user’sproduct knowledge is sufficient, the intention will besignificant, if not sufficient to execute the purchase. Whenthe user is satisfied with the choice and no longer wishes tocontinue searching for something better, he has, according tothe Theory of Planned Behavior (Fishbein and Ajzen 1975;Pavlou and Fygenson 2006), some positive web siteintentions: to make the purchase immediately or to returnto the web site later to possibly make the purchase. Thus,regardless of the UID,

H6 Choice satisfaction is positively related to web siteintentions.

Shopping enjoyment has been found to be a significantpredictor of a user’s behavioral intentions, including theuser’s intention to return to a web site (Koufaris 2002),intention to use it (Van der Heijden 2004), intention topurchase (Yen and Ng 2007) and intention to repurchase(Bauer et al. 2006). Thus, regardless of the UID,

H7 Shopping enjoyment is positively related to web siteintentions.

The overall nomological net for the theorized ante-cedents of web site intentions are presented in Fig. 1.

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3 Methods

We employed a 2×2 factorial design. There were two versionsof the web site (Attribute-Based UID, Question-Based UID)that were used for two products using one database size (54product versions). We chose 54 for the database size based ona secondary analysis of data reported in Kamis et al. (2008), aswell as additional data which was not used in that study. Inthis study, we report the results of 230 subjects who wererecruited from a panel of US-based online users maintainedby MarketTools’ Zoomerang, an online survey service. Thesample provided by Zoomerang was representative of thetotal US population of online users. We selected twoproducts for our experiment: backpacks and watches. Weselected backpacks and watches because they have bothfunctional and stylistic attributes and they are of similar,moderate complexity, thus attracting consumers to becomeinvolved in product customization. We built our own website to remove brand identification, to control the databasesize and to control the user interface. See the appendix forscreenshots of our Attribute-Based UID and Question-BasedUID. Overall, we designed a field experiment to enhanceboth internal and external validity, modeling the effects ofUID interventions on a representative sample of the onlineretail shopping population.

In the Question-Based UID version for both products, noproduct versions were displayed. Subjects were asked afixed sequence of eight questions to elicit their preferences.Then, they were directed to another web page, whichdisplayed the recommended product based on the answersto their questions. They were then given the opportunity tobacktrack and answer the questions differently. In theAttribute-Based UID version, the customizable attributesof the product were displayed on the left side of the webpage. The subjects were then able to change the settings forthe different attributes, making any combination of attributevalues, and click a button to display the customized producton the right side of the web page. The subjects could repeatthe process until they were satisfied with the result.

We first required the 230 subjects to customize a different,practice product, using both the Attribute-Based UID and theQuestion-Based UID versions with the same database size(54). In that way, when subjects were asked to use eitherversion of the web site for the watches and backpacks, theywould all start with the same learning experience, both inUIDs as well as database size. Once they finished the practicetask, they were assigned randomly to one of the fourconditions. To induce good participation, the subjects weregiven an incentive to take the tasks and surveys seriously.They were informed that after completing the experiment andsurveys, they would be eligible to win the item that theyselected or customized in a prize drawing.

For our survey instrument, we used and adaptedestablished scales for choice satisfaction (Huffman andKahn 1998), shopping enjoyment, perceived control (Ghaniand Deshpande 1994; Koufaris 2002) and purchaseintention (Hampton-Sosa and Koufaris 2005).

4 Results

The demographics of our sample indicated that our samplewas representative of the US online population (Group2002). Most of the 230 subjects were between 25 and56 years of age, 39% were male, 76% had some collegeeducation, and 64% had an annual household income of atleast $50,000.

To verify the random assignment of subjects to con-ditions, we conducted a multivariate analysis of variance.There were no significant differences in gender, age,education, or household income among the conditions.Since there were more women than men, we conducted ananalysis of variance to test whether there were anysignificant differences in our dependent variables betweenmales and females. None of the dependent variablesshowed any statistically significant difference. Since theresults for the watches and backpacks were not statisticallydifferent, we pooled their data for subsequent analysis.

ABUID (1)

or

QBUID (0)

Choice

Satisfaction

Shopping

Enjoyment

Perceived

Control

Web Site

Intentions

H1 (+)

H2 H6 (+)

H7 (+)

H5 (+)H3 (+)

H4 (+)

H2 (+)

Fig. 1 Research model and hypotheses

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4.1 Model

To test the research model, we conducted partial least squares(PLS) analysis using PLS-Graph version 3.0 (build 1130)with bootstrapping resampling (Bagozzi 1979; Fornell andLarcker 1981; Bagozzi and Yi 1988; Chin 2000; Chin et al.2003). The model, based on existing literature, has goodcontent validity: three indicators for Choice Satisfaction,three indicators for Perceived Control, four indicators forShopping Enjoyment and two indicators for Web SiteIntentions. The items of the survey instrument are presentedin Table 1, along with descriptive statistics.

Table 2 shows the descriptive differences between thetwo interfaces in perceived control, shopping enjoyment,choice satisfaction and web site intentions.

In addition, all constructs were confirmed to havesatisfactory convergent and discriminant validity, as show

in the cross-loadings of Table 3 (Hulland 1999; Chin et al.2003). Each variable’s items are more highly correlatedinternally, within the variable, than they are correlated withthe items of other variables. That is, the correlations on thediagonal, the Average Variance Extracted (AVE), are largerthan those off the diagonal. Although the off-diagonalcorrelations show that discrimination could be improved,there is adequate convergence within and discriminationamong the five variables. Overall, our measurement modelshows satisfactory validity (Jarvis et al. 2003).

The results of the PLS analysis of our model arepresented in Fig. 2.

The links show that the beta coefficients, analogous toregression beta weights, are all significant at a level of p<0.001, except for ABUID (1) or QBUID (0)→PerceivedControl which appeared to be significant at a level of p<0.01. We conducted a group analysis (Chin 2000) to verify

Table 1 Survey instrument, descriptive statistics and PLS measurements model

(Cron. Alpha, Comp. Relia.) (Mean, SD) PLS loading, t-stat

Choice satisfaction (1=extremely negative, 7=extremely positive) (0.891, 0.932)How satisfied or dissatisfied are you with the product you chose? (STSF1) (5.14, 1.769) 0.931***, 88.939I am very happy with the product I chose. (STSF2) (5.49, 1.854) 0.833***, 20.629I am very displeased with the backpack I chose. (reversed) (STSF3) (5.08, 1.905) 0.951***, 145.532Perceived control (1=strongly disagree, 7=strongly agree) (0.860, 0.914)While using the web site……I felt frustrated (reversed) (CONT1) (5.81, 1.596) 0.840***, 23.842…I felt in control (CONT2) (5.13, 1.914) 0.889***, 56.576…I felt confused (reversed) (CONT3) (5.48, 1.910) 0.921***, 57.753

Shopping enjoyment (1=strongly disagree, 7=strongly agree) (0.953, 0.966)While using the web site……I found my visit interesting (ENJ1) (4.96, 1.645) 0.945***, 105.540…I found my visit enjoyable (ENJ2) (4.98, 1.624) 0.943***, 101.470…I found my visit exciting (ENJ3) (4.19, 1.777) 0.895***, 55.879…I found my visit fun (ENJ4) (4.87, 1.762) 0.964***, 209.685Web Site Intensions (1=Extremely Unlikely, 7=Likely) (0.909, 0.957)If you actually had the money, how likely is it that you wouldbuy the product from this wed site? (PRCH)

(4.49, 1.912) 0.958***, 122.330

If you needed to purchase a similar product in the future, howlikely is it that you would return to this wed site? (RTRN)

(4.38, 1.978) 0.957***, 120.991

***p<0.001

Table 2 Descriptive statistics by user interface design

Perceived Control Choice Satisfaction Shopping Enjoyment Web Site Intensions

Mean SD Mean SD Mean SD Mean SD

Attribute-Based UID 6.13 1.07 5.99 1.14 5.33 1.21 5.01 1.56Question-Based UID 4.87 1.77 4.55 1.79 4.22 1.72 3.91 1.97

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that the two interfaces QBUID and ABUID do in fact havesignificantly different effects in the model shown in Fig. 2.H1, H3, H4, H5, H6 and H7 were found to be statisticallydifferent ( p<0.0001), indicating a stronger effect forABUID rather than QBUID; H2 was supported only at alevel of p<0.15. We conclude that six of the sevenhypotheses are strongly supported.

Sample size is an issue closely related to effect sizes. Theheuristic for sample size in a PLS model is to have at least tentimes the larger of either 1) the largest number of indicators forany construct or 2) the largest number of incoming links to anyconstruct (Chin and Newsted 1998; Chin 2000). According tothat heuristic, our model requires a minimum sample size of40, which is exceeded by our sample size of 230.

Figure 2 shows that influence did vary within the model.The influence of Attribute-Based UID (1) or Question-Based UID (0) on Perceived Control was moderately strong(beta=0.404). The influence of Perceived Control onChoice Satisfaction was slightly stronger (beta=0.447)and on Perceived Enjoyment stronger still (beta=0.675).The influence of Shopping Enjoyment was slightly weakerthan that of Perceived Control. On Choice Satisfaction, theinfluence was moderately strong (beta=0.320) and on WebSite Intentions, the influence was stronger (beta=0.525).The influence of Choice Satisfaction on Web Site Intentionswas moderate (beta=0.340). The variance explained is

moderate for Perceived Control (16.3%) and quite strongfor Shopping Enjoyment (45.6%), Choice Satisfaction(59.3%) and Web Site Intentions (63.1%).

5 Discussion

Our study examined a Flow-based nomological networkwhen online users use a particular UID to customizeproducts. We found that users of an Attribute-Based UIDexperienced significantly higher perceived control, shop-ping enjoyment and choice satisfaction than did Question-Based UID users. The model shows that the effects of anAttribute-Based UID on the decision outcomes, the WebSite Intentions, are indirect, through three key variables.The Attribute-Based UID improves the user’s perceivedcontrol, shopping enjoyment and choice satisfaction and inso doing improves the Web Site Intentions. The overallparsimonious model presented in this paper shows that it isfeasible to find strong linkages between a user technology,process and outcome variables, and behavioral intentions.

The results of this study provide significant theoreticalcontributions to UID research. First, we have demonstratedthe importance of examining the design of the user interfacewhen trying to explain and predict web site intentions. Wetested a model and found that Attribute-Based UID use

Table 3 Convergent-discriminant validity

Average Variance Extracted (AVE) Web SiteIntensions

ChoiceSatisfaction

ABUID (1) orQBUID (0)

ShoppingEnjoyment

PerceivedControl

Web Site Intensions 0.917Choice Satisfaction 0.694 0.822ABUID (1) or QBUID (0) 0.294 0.429 1.000Shopping Enjoyment 0.706 0.669 0.350 0.878Perceived Control 0.583 0.672 0.404 0.674 0.781

ChoiceSatisfaction

ShoppingEnjoyment

Perceived Control

Web SiteIntentions

** p<0.01

*** p<0.001

0.404***

0.136** 0.340***

0.525***

0.320***0.447***

0.675***

R2= 16.3%%

R2= 63.1%

R2= 59.3%

R2= 45.6%

ABUID (1) or

QBUID (0)

Fig. 2 Results of PLS analysis

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increases web site intentions indirectly, through perceivedcontrol, shopping enjoyment and choice satisfaction.However, the superior influence of the Attribute-BasedUID is through Perceived Control only, i.e., not throughChoice Satisfaction. The Attribute-Based UID, an interac-tive technology that addresses both utilitarian and hedonicuser needs, successfully extends and refines Flow modelingin the online shopping context. The parsimonious model,which explains up to 63.1% of the variance in the model’svariables, shows that the customer’s web site intentions aresignificantly improved with an Attribute-Based UID ratherthan a Question-Based UID. This may be somewhatsurprising, since users are usually reluctant to exertsignificant cognitive effort (Todd and Benbasat 1992;Kamis 2006). When putting more effort into the process,there are higher expectations which may lead to lesssatisfaction, and less enjoyment with the process (Gretzeland Fesenmaier 2005). A Question-Based UID requires lesscognitive effort than an Attribute-Based UID. Our findingssuggest that the benefits of an Attribute-Based UID areworth the additional cognitive effort.

Other research has found nomological networks centeredon web site use and usability to be strongly predictive ofactual purchase behavior. For example, Venkatesh andAgarwal (2006) had users examine four types of productswith web sites from diverse industries, including automo-biles, car rentals, airline reservations and books, and thenrespond to a telephone survey six months later. Web siteusability was assessed with Microsoft Usability Guidelines(Agarwal and Venkatesh 2002). The study did not controlfor different forms of decision support at the various websites, instead letting decision support vary. The varianceexplained was 53% for both web site use and actualpurchase. The most significant predictors of web site use, inorder of significance, were usability in the categories ofcontent and made-for-the-medium, followed by ease of use,previous product purchase on web site and emotionassociated with web site usability. The most significantpredictors of actual purchase were web site use, followedby usability in the categories of content and made-for-the-medium, previous product purchase on web site andpurchase need. The current study suggests that web siteusability for some shopping categories examined inVenkatesh and Agarwal (2006), particularly automobiles,could benefit further from an Attribute-Based UID.

The results of our nomological net complement decisionsupport system research which uses complex models tobetter understand how and why UID impacts decisionmaking (Bharati and Chaudhury 2004; Chenoweth et al.2004; Kamis 2006). One important contribution of ourstudy is in examining interface design, an antecedent ofperceived control. Research has shown that hedonicphenomena, such as perceived control or perceived enjoy-

ment, are important beliefs antecedent to user attitude(Hong et al. 2008). This study focuses attention on theinterface design characteristics, an antecedent of thosehedonic phenomena.

For companies that have user-led customization capa-bilities which they wish to provide to customers visitingtheir web sites, our results show that an Attribute-BasedUID can be effective. Users do well with an interface thathelps them identify the customizable product attributes,change their values, and interactively experiment until theyare satisfied. Companies such as Dell, Nike and Lands’ Endoffer such an interface, using technology from vendors suchas Archetype Solutions and Scene7.

Finally, our results also indicate that providing anappropriate Attribute-Based UID on a web site can createan environment in which customers enjoy their shoppingexperience more and perceive the process as improvingtheir choice satisfaction. Consequently, customers maymake more purchases or return to the online business forfuture purchases, thus developing a long-term relationship.

6 Limitations and future research

One limitation of our study is that we studied only twoproduct categories: watches and backpacks. We do notview this as a major limitation, since watches andbackpacks appeal to men and women, and they are ofmoderate complexity, neither highly differentiated norhighly undifferentiated, i.e., commodity-like. Future re-search could test other product categories, including thosewith attributes which cannot be represented and commu-nicated easily via sight or sound. Examples would includeany products which rely on the use of smell, taste, andtouch, e.g., cookies, flowers or clothing made withunusual fabrics.

Another limitation of the current study is the number ofchoice alternatives due to database size. We tested only onedatabase size: 54 choice alternatives. It is possible that theuser experience varies with different database sizes. Ideally,one would design an experiment in which each attribute isenabled to be set to any attribute value, thus populating thedatabase with all possible combinations. Such an experi-ment, however, would require considerably more than 230subjects. Future research could also examine the impact ofa gradual increase in the each attribute value on the userexperience.

While our sample consisted of real web users represen-tative of the US online population, it also limited ourcontrol over the experimental procedure. Each subject wasinvited to participate via email, and it is possible that theparticipation of some subjects was influenced by external,e.g., social, influences. We required subjects to provide a

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unique ID number given to them with the email invitationin order to prevent multiple instances of the same subject.We did monitor the time stamps of each step of the studyfor each subject and eliminated subjects who participated inthe experiment over multiple time periods. Future researchshould test empirically the effects of non-US cultures, sincethey have been theorized to moderate the adoption ofelectronic commerce (Zhang and Maruping 2008).

Finally, our results may be specific to product customi-zation by online users in the residential context. Ourfindings may only apply to non-organizational settingsand preferential choice tasks (Payne 1976; Todd andBenbasat 1992). Logical extensions to this study includethe testing of Attribute-Based UID on (1) experiential,hedonic vs. focused, goal-directed shopping (Van derHeijden 2004; Hassanein and Head 2005), (2) exploratorybrowsing vs. confirmatory, knowledge-based shopping(Moe 2001; Moe and Fader 2004), (3) goods vs. servicesof the same complexity in the same industry and (4) goodswith standardized components and interfaces, e.g., com-puters, vs. goods with non-standard components andinterfaces.

User-led customization via Attribute-Based UID use is notrelevant only to online shopping. User-led customization hasbeen deployed in many industries, including automobiles (Roet al. 2007), footwear (Dietrich et al. 2007), apparel (Ivesand Piccoli 2003), furniture (Yao and Carlson 2003),

architecture and construction (Richard 2007) and educa-tional training (Cohen and Pine 2007). Any industry, itseems, with products or services of moderate to highcomplexity is a candidate for user-led customization. Suchsystems may even be used for work tasks having objective,optimal solutions. In such systems, we suspect that thehedonic perceptions of the workers would be less importantto their employers than productivity. On the other hand, wor-kers experiencing flow may be more productive than workerswho are not (Csikszentmihalyi and Csikszentmihalyi1988).

In online commerce, a customer can switch to acompany’s competitors quite easily. US corporations losehalf their customers within five years, and the loss of thesecustomers reduces corporate performance by 25 to 50%(Reichheld 1996). Research has also found that with eachadditional year in a relationship between a company and acustomer, the customer becomes less costly to servicebecause of learning effects and decreased servicing costs.Over time, loyal customers buy more, sometimes atpremium prices, and provide referrals through positiveword-of-mouth (Keaveney 1995; Reichheld 1996). Userself-customization of products (or services) may not onlydecrease the cost of selling and increase the enjoyment ofbuying, but it may also increase customer loyalty anddecrease customer acquisition costs. These linkages are onour agenda for future research.

Appendix

Screen Shots of the Question-Based UID and Attribute-Based UID

Question-Based UID Attribute-Based UID

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Arnold Kamis is an Associate Professor in Information Systems atSuffolk University in Boston, MA. He received his Ph.D. in InformationSystems from the Stern School of Business, New York University.Arnold’s research interests include decision support technologies andhuman-computer interaction. His publications appear in MIS Quarterly,Information & Management, International Journal of ElectronicCommerce, Communications of the ACM, Communications of the AIS,and DATABASE.

Tziporah Stern is a doctoral student in Baruch College, City Universityof New York. She received her B.Sc. in Computer Science from TouroCollege in New York. Her research interests include information privacyand e-commerce. Tziporah’s publications appear in MIS Quarterly,the Encyclopedia of Multimedia Technology and Networking, and inthe proceedings of the Northeast Decision Sciences Institute and theAmericas Conference on Information Systems.

Daniel M. Ladik is an Assistant Professor of Marketing at SuffolkUniversity in Boston, MA. He completed his Ph.D. at the University ofSouth Florida in Tampa, FL. Dan’s research interests include technology-based self-service, sales management, and marketing strategy. His workappears in Journal of the Academy of Marketing Science, IndustrialMarketing Management, Journal of Business and Industrial Marketing,Journal of Marketing, Theory, and Practice, and Marketing Management.

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