Online shopping viewed from a habit and value perspective

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Online shopping viewed from a habit and valueperspectiveSeppo Pahnila a & Juhani Warsta aa Department of Information Processing Science , University of Oulu , Oulu, FinlandPublished online: 24 Jun 2010.

To cite this article: Seppo Pahnila & Juhani Warsta (2010) Online shopping viewed from a habit and value perspective,Behaviour & Information Technology, 29:6, 621-632, DOI: 10.1080/0144929X.2010.501115

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Online shopping viewed from a habit and value perspective

Seppo Pahnila and Juhani Warsta*

Department of Information Processing Science, University of Oulu, Oulu, Finland

(Received 30 June 2008; final version received 7 June 2010)

In this article, we study e-commerce customer behaviour towards online shops. The theoretical model is basedon Triandis’ behavioural framework. Prior value research has mostly focused on users’ attitudes towards onlineshopping. We explore the role of perceived value and habit in e-commerce behaviour. Structure equation resultssuggest that the utilitarian as well as the hedonic values have a significant impact on affect, and indirectly alsoon e-commerce behaviour. We also assessed the importance of habit on shoppers’ online behaviour. Accordingto our results, online shoppers’ habitual behaviour has a significant impact on affect. We also found thatnormative beliefs (social factors) are the preceding factor of habit in cases in which the shopping experience isnot recurrent.

Keywords: e-commerce; value; habit; social factors; affect; Triandis

1. Introduction

The rapid and continuous growth of Internet shoppinghas forced service providers to pay considerableattention to factors affecting consumer behaviour.Service providers also have to consider that the useof the Internet is changing from earlier users’ emphasison functional aspects of the Internet. As new mediahave spread into homes and users have become moreexperienced with online shopping, these users havestarted to appreciate features which relate to emotionalexperiences (Bridges and Florsheim 2008). By the late1990s, Bellman et al. (1999) had already started talkingabout the ‘wired’ lifestyle, i.e. customers who havebeen using the Internet for years.

The purpose of this research is to study theimportance of those issues that have an influence onusers’ e-commerce behaviour. Our research model isbased on Triandis’ theoretical framework, in whichbehaviour is seen as a consequence of affect andintentions (Triandis 1980). In this article, we studythe influence of habitual behaviour usage andmotivational issues – utilitarian and hedonic values– on online behaviour. Habit can be understood as‘learned sequences of acts that become automaticresponses to specific situations which may befunctional in obtaining certain goals or end states’(Verplanken et al. 1997). Utilitarian values reflectextrinsic, non-emotional motivational factors likegetting a low price, ease of use or usefulness.Hedonic values reflect intrinsic, emotional motiva-tional aspects relating to positive or negative

e-shopping experiences, such as enjoyment, satisfac-tion or anxiety (Bridges and Florsheim 2008).

We conducted a field study based on a webquestionnaire which resulted in 211 reliable responses.Our findings suggest that normative beliefs have asignificant impact on habit and habit further signifi-cantly explicates affect. Both the utilitarian value andthe hedonic value have significant effects on affect. Thisarticle is organised as follows. In the following section,we will discuss, based on prior literature, issues whichmay have an influence on affect, intentions ande-commerce behaviour. In Section 3, we introducethe research methodology. In the final section, we drawconclusions from the results of the study.

2. Theoretical background

The research model described in Section 3.3 is based onTriandis’ behavioural framework (Triandis 1980). Thebasic elements of our model are affect, intentions andbehaviour. Behaviour refers to the consequences of aperson’s feelings about a situation. According toTriandis’ model, two dimensions affect the behaviourof the individual: issues that are related to internalfactors, such as personality, and environmental mat-ters, such as social pressure. An individual’s perceptionabout the outcomes and prior, goal-oriented behaviourmay act as a trigger to action that may lead to actualbehaviour (Triandis 1980).

Affect refers to an individual’s emotional feelings:the ‘feeling of joy, elation, or pleasure, or depression,

*Corresponding author. Email: juhani.warsta@oulu.fi

Behaviour & Information Technology

Vol. 29, No. 6, November–December 2010, 621–632

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DOI: 10.1080/0144929X.2010.501115

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disgust, displeasure, or hate associated by an indivi-dual with a particular act’ (Triandis 1980). Thus, affectis a motivational factor that may reinforce theindividual’s intention toward behaviour. This is tosome extent consistent with the term attitude in theTheory of Reasoned Action (TRA) (Fishbein andAjzen 1975, Fishbein 1980). Emotions are charac-terised by the same elements as hedonic value, andconsequences have similarities with utilitarian values.Intentions indicate how hard individuals are willing totry to perform a behavioural act; the stronger theintention to carry out the behaviour, the more likely itis to be carried out. In our study, the more likely theindividuals perceive the positive consequences of usinge-commerce to be, the more probable it is that they willuse it.

2.1. Value construct

Analysing the vendor and buyer relationship, customervalues and perceived shopping values are salientaggregate concepts that reflect the success and con-tinuity of this relationship. Customer value has beendefined differently over the years depending on theresearch focus (Zeithaml 1988). Value is one of theimportant key elements offering competitive advantagefor companies which are changing their developmentactivities from organisational improvements to a moreoutward-facing orientation, focusing towards theircustomers (Woodruff 1997). Customer value can alsobe seen from a quite conceptual level, as Collins et al.(2007) remind us: ‘values are general and abstract andrefer to what individuals find important for their ownlife in general’. Customer value may also becomeevident through emotional bonds that lead thecustomers to buy repeatedly from the supplier; this isone of the prime objectives for retailers. This leads tothe requirement to truly understand the customer andthe different levels and aspects of customer value (Butzand Goodstein 1996).

Research around the value concept has pros-pered, especially since online shopping has becomecommon. New avenues for research were opened, asthe retailers did not understand for certain howcustomers would take this new channel, how theywould use it and what the requirements for its usewere. Keeney (1999) elaborated specific categories ofobjectives influenced by purchases from the Internet.He gives a definition of the value propositionassociated with Internet commerce ‘as the net valueof the benefits and cost of both a product and theprocesses of finding, ordering and receiving it.’According to Lumpkin and Dess (2004), the valueadding activities of the Internet are: search, evalua-tion, problem solving and transaction.

2.2. Shopping channels and utilitarian and hedonicvalues

Utilitarian and hedonic value concepts have been usedin the analysis of different environments and channelsas shown in Table 1. Earlier, research on utilitarianand hedonic values concentrated on conventional brickand mortar channels that are represented nowadays byshopping malls (Babin and Attaway 2000, Stoel et al.2004, Jones et al. 2006). Babin and Attaway (2000) usetheir structural model to explain how positive andnegative affects do not have a direct effect on customershare (repeated purchasing behaviour), but aremediated via the perceived utilitarian and hedonicshopping values. Jones et al. (2006) construct twoseparate models in which utilitarian and hedonicshopping values are used to explain retail variablessuch as customer satisfaction, word of mouth, repa-tronage and loyalty. They find that utilitarian shoppingvalue might be a necessary, but not sufficient conditionfor building store loyalty. Furthermore, hedonic valuerepresents the emotional worth of the shoppingexperience. Both utilitarian and hedonic values explaincustomer satisfaction, though hedonic shopping valueis significantly stronger. The hedonic value explains theword of mouth, repatronage anticipation, and loyaltyvariables, whereas utilitarian value explains repatro-nage intention. Stoel et al. (2004) use mall attributebeliefs, time spent and money spent to explainutilitarian and hedonic values, and further facilitatethese values to explain repatronage intentions. Mallattribute beliefs accurately explain time spent, utilitar-ian and hedonic values, but what is interesting is thatthe hedonic shopping value accurately explainedrepatronage intentions, whereas the utilitarian valuedid not support this relationship.

The Internet became widespread during the 1990sand provided a new and a fast expanding informationand trading channel for old brick and mortarcompanies as well as for new Internet-based firms.This phenomenon catalysed research to analyse andcompare possible differences that exist between thesedistinct channels based on the old research traditionand its methods (Childers et al. 2001, Fiore et al. 2005,Overby and Lee 2006, Bridges and Florsheim 2008).Childers et al. (2001) use navigation, convenience, andsub-experience to explain the utilitarian and hedonicvalues and ease of use. These three last variables arealso used to understand consumer shopping behaviour(attitudes toward interactive shopping). According totheir findings, utilitarian and hedonic shopping valuesrelate positively with attitudes, although they differbetween distinct categories of the interactive shoppingenvironment. Fiore et al. (2005) study how imageinteractivity technology affects instrumental

622 S. Pahnila and J. Warsta

Table 1. Utilitarian and hedonic values explain in different channels. A plus (þ) sign indicates a positive effect.

Reference Utilitarian value explains Hedonic value explains Channel Findings

Babin and Customer share Customer share Brick and mortar Positive and negativeAttaway(2000)

þ þ affect alters perceivedhedonic and utilitarianshopping value, andthrough this perceivedvalue customer shareis affected.

Childers et al. Attitude Attitude Internet shopping While the instrumental(2001) þ þ aspects of new media

are importantpredictors of onlineattitudes, the moreimmersive, hedonicaspects of new mediaplay at least an equalrole.

Mathwick el al.(2001)

EfficiencyEconomic value

Visual appealEntertainmentEscapismEnjoymentþ

Catalogue andInternetshopping

Internet shopping maybe leading to acommoditisation ofproducts and services,with an emphasis oncost reduction overbrand-baseddifferentiation.

Catalogue shoppingappears to be basedupon a broader rangeof experiential valuesources.

Stoel et al. (2004) Repatronage intention Repatronage intentionþ

Brick and mortar Hedonic value ispositively linked withintention to returnto the mall.

Utilitarian value doesnot necessarily havethis dependency.

Bruner II andKumar(2005)

Attitude toward the actaffects (behaviouralintention)

Attitude toward the actaffects (behaviouralintention)þ

Mobile commerce/Internet shopping

Hedonic aspectcontributes more thanutilitarian aspect toconsumer adoptionof Internet devices.

Fiore et al. (2005) Attitude toward theonline retailer

Attitude toward theonline retailer

Internet shopping Image interactivitytechnology provides

Willingness to purchasefrom the online retailer

consumers withinstrumental

Willingness to patronisethe online retailerþ

(utilitarian) valuefrom enriched productinformation and theconvenience andexperiential (hedonic)value of an engagingshopping experience.

Noble et al.(2005)

Channel utilisationþ

N/A Brick and mortar,catalogue andInternet shopping

Significant differencesfound in theunderlying utilisationvalues influencingconsumer channelutilisation.

(continued)

Behaviour & Information Technology 623

(utilitarian) and experiential (hedonic) values thatfurthermore explain consumers’ attitudes towards theonline retailer, willingness to purchase from the onlineretailer and willingness to patronise the online retailer.Here, again, only the instrumental value showed astrong relationship with these variables. In the modeldeveloped by Overby and Lee (2006), utilitarian andhedonic values explain online shopping preferencesand intentions. They also find that utilitarian value ismore strongly related than hedonic value to thepreference towards the Internet retailer and intentions.Bridges and Florsheim’s (2008) study also supports theopinion that the utilitarian flow elements affect onlinepurchasing positively, while they found hedonicelements of flow to be unrelated to online shopping.

The trading channel that most closely resembles theInternet as a channel is the catalogue business, wherethe customer gets information by flipping through apaper catalogue and making orders based on thisinformation. The Internet replaces the catalogue,giving more versatile ways of acquainting oneselfwith the products and services provided by the firms(Mathwick et al. 2001, Noble et al. 2005). Mathwick

et al. (2001) further confirmed the attitudes that theperceived return on financial, temporal and behaviour-al investment (utilitarian values) were found to besignificantly related to preferences for online shopping.Interestingly, ‘catalogue shopping appears to entertainand delivers visual appeal that is either missing from,or, was not noticed in the online context’ (Mathwicket al. 2001). This raises the question of whether theusers of these different channels value different mattersin the shopping process. Noble et al. (2005) were ableto confirm these findings in their study, which coveredthree different channels: brick and mortar, catalogueand online shopping. They also found notable differ-ences in utilitarian values affecting consumer channelutilisation.

At the moment, the newest channel is the Internetcombined with mobility, that is, getting informationand placing orders using a mobile device, instead ofsitting in front of a tabletop computer (Bruner II andKumar 2005, Kleijnen et al. 2007). Bruner II andKumar (2005) use their c-TAM model to describe howthe fun factor (hedonic value) is emphasised comparedto usefulness (utilitarian value), in explaining a

Table 1. (Continued).

Reference Utilitarian value explains Hedonic value explains Channel Findings

Jones et al. (2006) SatisfactionRepatronageintentions

SatisfactionWord of mouthRepatronageanticipation

Loyalty

Brick and mortar Utilitarian shoppingvalue might be anecessary but notsufficient conditionfor building storeloyalty. Hedonicvalue represents theemotional worth ofthe shoppingexperience.

Overby and Lee(2006)

Preference affectsIntentionsþ

Preference affectsIntentions

Internet shopping Utilitarian value ismore strongly relatedthan hedonic value topreference towardsthe Internet retailer,and intentions andshopping frequencycan play a moderatingrole.

Bridges andFlorsheim

Online flowþ

Online flow Internet shopping Utilitarian flow elementsincrease purchasing.

(2007) Hedonic elements offlow are unrelated toonline buying.

Kleijnen et al.(2007)

Intention to useþ

N/A Mobile commerce/Internet shopping

Time convenience is themost importantbenefit. User controlover the servicedelivery processaffects utilitarianvalue perceptions.

customer’s attitude toward the act of using the system.Kleijnen et al. (2007) found that time, convenience anduser control over the service delivery process affectutilitarian value perception. Their study supports theview that the value of the mobile channel significantlyand positively affects intention. Customers considercompetition with alternative channels to be betweentraditional brick and mortar and electronic (onlineshopping, be it mobile or Internet) channels.

2.3. Social factors

The TRA has proven successful in predicting andexplaining behaviour across a wide variety of domains(Davis et al. 1989). According to the TRA, a person’sperformance of a given behaviour can be defined usingthe formula:

BI ¼ Aþ SN

where BI is the person’s behavioural intention toperform a given behaviour, which is jointly determinedby the person’s attitude (A) and subjective norm (SN).The normative component SN defines the influence ofthe social environment on behaviour, where SN isdetermined by the person’s normative beliefs (socialfactors), that is, perceived beliefs that a reference groupor individual thinks whether the person should or shouldnot perform behaviour BI. Behavioural intention meansthe person’s subjective probability that he will perform agiven behaviour (Fishbein and Ajzen 1975). TRAexplains that individual will perform a behaviour if heor she has a feeling that there could be positive benefits(outcomes), for example finding a lower price, associatedwith actual behaviour during online shopping (Compeauand Higgins 1995). Thus, TRA is based on reasoning.Individuals make their decisions consciously as a resultof thorough deliberation.

Aydin and Rice (1991) suggest that individualscreate their behaviour towards the information systembased on their interaction with each other andmembership of a social environment or the influenceof important people may have a persuasive influenceon whether or not to perform a specific behaviour.Karahanna et al. (1999) found that social factors havean influence on the intention to adopt informationtechnology.

As mentioned earlier, habits are defined as auto-matic, goal-oriented behaviour, which is based on alearning process. However, when the behaviour hasnot yet become established, the physical environmentmay have an influence on an individual’s decision-making process (Aarts et al. 1997). In their study, theyargued that persuasive messages or social pressure mayhave a positive effect on an individual’s decision as to

whether or not to exercise. Thus, the views of othersmay influence an individual’s prior assessment. If theindividual has little or no previous experience withthe behaviour, then the normative effect may help theindividual in his or her decision-making process (Aartset al. 1997). It can be supposed that colleagues’ andpeers’ recommendations or persuasion may act asenvironmental stimuli, having an effect on individuals’habits. Recommendations may enhance the strength ofhabit. Thus, we can hypothesise:

H1: Social factors have an impact on habit.

2.4. Habit

A habit is unconscious or automatic behaviour, asopposed to intentions, or conscious behaviour(Triandis 1980, Limayem and Hirt 2003, p. 71). Habitsdiffer from reflexes as, in order to become a habit, anactivity requires learning that is composed of severalfactors, such as the number of short-term repetitions,reinforcement, the clarity of the situation, interest andability to learn and so on (Triandis 1980). Thus, habitis considered as an unconscious construct explainingbehaviour (Kim and Srivastava 2007).

According to Ajzen (2002), habitual behaviour isdefined by past behaviour: the more frequently abehaviour has been performed in the past, the strongerthe habit. This is consistent with Triandis (1980). Hesuggests that the strength of habit ‘can be measured bythe frequency of occurrence of behaviour’. Ajzen(2002), on the other hand, suggests that the frequencyof past behaviour, the number of times that the pastbehaviour has been performed, is not an indicator ofhabitual behaviour. He suggests that it is very difficultto demonstrate when habit strength is related tofrequent behaviour. Furthermore, he emphasises thatconscious control is an important issue, which has aneffect on habitual behaviour. Individuals always usedeliberation in their decision to take a given action.This perspective, using reasoning, is consistent with theTRA (Fishbein and Ajzen 1975). Verplanken et al.(1997) emphasise that when behaviour is performedrepeatedly and becomes habitual it may lose itsreasoned-based antecedents. Rather than reasoning,habitual behaviour is based on goal-oriented beha-viour. According to Ajzen (2002), the association ofpast behaviour frequency and frequency of laterbehaviour only indicates that the behaviour in questionis stable over time, not that it is habitual behaviour.Furthermore, he argues that past behaviour is a goodpredictor of later behaviour only in demonstrating thatthe behaviour is stable over time.

Based on Triandis’ model, habits are found toexplain IS usage (Limayem and Hirt 2003, Cheung andLimayem 2005). It is argued that the influence of habits

on actual behaviour increases in the long run, while theinfluence of behavioural intentions decreases (Limayemand Hirt 2003, p. 84) in the long run. Hence, it isproposed that technology use can be made habitual byinitially making it mandatory, or by introducing rewardsand other incentives for the use of the technology.Verplanken et al. (1997) demonstrated that strong habitsreduce the need for decision-making information andalso simplify information search strategies. Furthermore,they found that in complex decision-making contexts,strong habits could only temporarily be overruled.Following this lead, we suggest that habitual behaviourexplains e-commerce behaviour:

H2: Habit has an impact on consumer affect towardse-commerce behaviour.

2.5. Utilitarian and hedonic value

The general value concept is further specified to consistof the utilitarian and the hedonic value dimensions,indicating an assessment of the overall worth ofshopping activity (Babin and Attaway 2000, Senecalet al. 2002, Kurki et al. 2007). According to Eggert andUlaga (2002), customer-perceived value relates tosatisfaction, and they confirm that these concepts aretwo complementary, yet distinct constructs. Theutilitarian value is characterised in the online contextby task-related worth, one-click purchasing, intuitivesearch engines, usefulness, information attainment,price comparison and assortment describing attributes.Thus we can hypothesise:

H3: Utilitarian value has an impact on consumer affecttowards e-commerce behaviour.

The hedonic value, on the other hand, reflectsworth found in the shopping experience itself, asidefrom any task-related motives, the flow constructembodying aspects of fun and playfulness, and multi-sensory, fantasy and emotive aspects of the shoppingexperience (Babin and Attaway 2000, Childers et al.2001, Noble et al. 2005, Jones et al. 2006, Bridges andFlorsheim 2008). Overby and Lee (2006) defineutilitarian value concisely as reflecting functionalbenefits and sacrifices, and hedonic value as describingexperiential benefits and sacrifices, such as entertain-ment and escapism. From this we construct thefollowing hypothesis:

H4: Hedonic value has an impact on consumer affecttowards e-commerce behaviour.

3. Research methodology

We ensured content validity by measuring all items inthe instrument using a seven point Likert scale

(strongly disagree – strongly agree). Since not all themeasures used in this study have been tested in thee-commerce environment, the present research teststhese measures in this context, and provides newinsight in this respect, as well. Hence, the questionswere first pilot tested using 35 people and, based ontheir feedback, the content validity of the questionswas checked and improved.

The use of a Likert-type scale is often discussedamong researchers because it is, in a purely statisticalsense, categorised as a non-parametric ordinal scale.However, in behavioural research it is commonlytreated as a parametric scale, similar to an intervalscale. An ordinal scale does not indicate the exactdifference between the points on the scale. Whenresearching beliefs and attitude, it is difficult todefine the exact distances between the points. It canbe presumed that the distance between, for example,points 1 and 2 describes a similar difference in thephenomenon under examination as the differencebetween points 6 and 7. In particular, when thenumber of respondents is adequate, and distancesbetween the points are proposed to be regular andthe scale reminds one of an interval scale (Agresti1990). Literature suggests variety of optimal numberof scale points (Cronbach 1950, Komorita andGraham 1965, Mattel and Jacoby 1971, Rasmussen1989). In general, the identification of the measure-ment scale is meaningful because it helps to separatenon-metric data from metric data and secondly, themeasurement scale used influences the selection of anappropriate method for analysis of the data (Hairet al. 1998).

As mentioned earlier, the questions used in thisresearch are based on literature and prior research; thequestions are validated in other research, (Table 4).According to Straub (1989) and Boudreau and Gefen(2001), one can improve the reliability of constructsand results by using validated and tested questions.

3.1. Demographic profiles

Our target group was students and users of theInternet. A field study was conducted, gaining 220responses. The total sum of reliable responses was 211.Table 2 summarises respondents’ descriptive statistics.The demographic data shows that the number of malesis 68.2% and females is 29.9%. Most of therespondents are young, 51.6% representing the agegroup 22–31 years and 19.4% representing the agegroup less than 22 years. Most of the respondents(69.7%) assess that they use e-commerce services lessfrequently than once a week. 68.2% of the respondentsassess their e-commerce usage time being less than 0.5hours in a week. Respondents’ computer expertise is

rather high, 43.6% assess their expertise to be fairlygood and 46% very good.

3.2. The measurement model

Descriptive statistics of the study was analysed usingSPSS 16.0 software package. The data analysis wasconducted using the Smart Partial Least Squares (PLS)structural equation modelling (SEM) technique (Ringleand Wende 2005). PLS has been widely used andaccepted in different contexts and disciplines (Limayemet al. 2000, Venkatesh et al. 2003, Kleijnen et al. 2007).

There are two primary methods of SEM analysis.The first approach is covariance-based analysis, whichtypically uses as default the maximum likelihood (ML)

method. For example, ML is employed in LISREL, EQSand AMOS statistic software (Gefen et al. 2000). Thesecond is variance- or component-based analysis em-ployed in PLS, which is designed to explain variance.Covariance-based SEM techniques are appropriate insituations where prior theory is strong and stable (Chinand Newsted 1999). Therefore, covariance-based full-information method (i.e. ML) is best suited forconfirmatory, theory testing research. In contrast, PLSis more suited for predictive research and theorydevelopment (Chin and Newsted 1999). Therefore,some researchers have suggested PLS as a complemen-tary technique to covariance-based techniques (Gefenet al. 2000). PLS applies as default a bootstrap method,which is an iterative algorithm consisting of a series ofordinary least squares analysis and multiple linearregression, analysing one construct at a time whileminimising the residual variance of all dependentvariance in the model. Thus, it differs from thecovariance-based techniques, which estimate the varianceof all the observed variables at a time (Gefen et al. 2000).

Bootstrapping is a resampling procedure in whichthe researcher’s original sample is treated as thepopulation. Cases from the original data set arerandomly selected with replacement to generate otherdata sets, usually with the same numbers of cases as theoriginal. Therefore, because of the replacement, thesame case may be selected more than once in agenerated data set. When repeated many times, forexample in this study 300 times, bootstrappingsimulates the drawing of numerous samples from apopulation (Kline 1998).

It is suggested that PLS is a powerful pathmodelling procedure because of minimal demands onmeasurement scales (i.e. categorical to ratio levelindicators can be used in the same model), samplesize and residual distributions (Simon and Bruce 1991,Chin and Newsted 1999).

One of the main reasons why component-based PLSwas used in this study, instead of covariance-based andexplanatory SEM techniques, is the starting point of theresearch. First, this research is more predictive thanconfirmatory theory testing by nature. Second, the

Table 2. Profile of the respondents.

Measure Items FrequencyPercent

Gender Male 144 68.2Female 63 29.9Missing 4 1.9

Age 522 41 19.422–31 109 51.632–41 24 11.442–51 21 10.0451 16 7.6

Frequency ofe-commerceuse

Not at all 13 6.2Less than once a week 147 69.7About once a week 31 14.72 or 3 times a week 13 6.2Several times a week 4 1.9About once a day 0 0Several times a day 2 0.9Missing 1 0.5

Computerexpertise

Very weak 0 0Fairly weak 0 0Average 22 10.4Fairly good 92 43.6Very good 97 46.0

E-commerceusage timeper week

50.5 h 144 68.2Less than 1 h 45 21.3l–5 h 18 8.65–10 h 1 0.5More than 10 h 0 0Missing 3 1.4

Table 3. The mean, standard deviation and correlations of the constructs.

Construct Mean Standard deviation 1. 2. 3. 4. 5. 6. 7.

1. Behaviour 1.86 0.69 12. Intentions 4.94 1.38 0.441 0.8673. Affect 5.32 0.86 0.199 0.583 0.7254. Utilitarian value 5.40 0.92 0.246 0.553 0.581 0.8255. Hedonic value 2.45 1.24 0.238 0.266 0.319 0.137 0.8386. Habit 3.51 1.19 0.437 0.781 0.570 0.548 0.285 0.8367. Social factors 3.31 1.56 0.184 0.295 0.262 0.235 0.221 0.250 0.810

Note: The diagonal elements in italics are square roots of the average variance extracted.

selected resampling method frees us from the assump-tion that the data conforms to a bell-shaped curve.

The mean, standard deviation and correlations ofthe constructs are shown in Table 3. The contentvalidity of the instrument was ensured by carrying outa pilot test with IT students. Convergent validity wasensured by assessing the factor loadings and bycalculating variance extracted.

Discriminant validity indicates the extent to whicha given construct differs from other constructs. Foradequate discriminant validity, the square root of thevariance extracted should be greater than the correla-tion of the constructs. Discriminant validity wasassessed by computing the correlations between con-structs, and the correlations between all pairs of

constructs were below the threshold value of 0.90(Hair et al. 1998). The square root of the varianceextracted was greater than the correlations of theconstructs (Table 3). Hence, the reliability and validityof the constructs in the model are acceptable.

Table 4 presents all the research constructs anditems, convergent validity, internal consistency andreliability. As Table 4 shows, all the model itemsloaded well, values exceeding 0.50 (Hair et al. 2006).Internal consistency reliability among the items wasassessed by calculating Cronbach’s a. Table 4 showsthat this coefficient exceeds the suggested value of 0.60for all constructs (Nunnally 1978, Hair et al. 2006),except in the case of Habit2 and Habit3, which weredropped. The variance extracted of all the constructs

Table 4. Convergent validity, internal consistency and reliability.

Construct ItemsFactorloading

Averagevarianceextracted

Cronbach’sa

Compositereliability

Intentions Intent1 I will do online shopping on aregular basis in the future.

0.871 0.752 0.836 0.901

Triandis (l980),Thompson et al.

Intent2 I will frequently do onlineshopping in the future.

(1991) Intent3 I will strongly recommend othersto do online shopping.

Affect Affect1 Doing online shopping is smart. 0.743 0.526 0.707 0.816Triandis (l980),Limayem and

Affect2 Doing online shopping is enjoyable. 0.795

Hirt (2003) Affect3 Doing online shopping is boring. 0.693Affect4 Doing online shopping is fun. 0.665

Utilitarian valueOverby and Lee(2006)

Utvalue1 The prices of the product and/orservices I purchased from the onlineshop are at the right level,given the quality.

0.863 0.680 0.765 0.864

Utvalue2 The products and/or services I purchasedfrom the online shop were a good buy.

Utvalue3 The online shop offers a goodeconomic value.

Hedonic value Hedval1 Making a purchase totally absorbs me. 0.788 0.701 0.860 0.903Overby and Lee(2006)

Hedval2 The online shop doesn’t just sellproduct or services – it entertains me.

Hedval3 Making a purchase from an onlineshop ‘gets me away from it all’.

Hedval4 Making a purchase from an onlineshop truly feels like ‘an escape’.

Habit Habit1 Online shopping has become ahabit for me.

0.804 0.699 0.788 0.875

Limayem and Habit2 I am addicted to online shopping. DroppedHirt (2003) Habit3 I must do online shopping. Dropped

Habit4 I don’t even think twice beforedoing online shopping.

Habit5 Doing online shopping has becomenatural to me.

Social factors Socfact1 People in my organisation haverecommended me to do online shopping.

0.644 0.656 0.743 0.849

Fishbein andAjzen (l975)

Socfact2 My peers have recommended me todo online shopping.

Socfact3 My immediate friends have recommendedme to do online shopping.

exceeded 0.5 (Fornell and Larcker 1981, Hair et al.1998). The composite reliability of all the constructsexceeded the suggested value of 0.7 (Nunnally 1978).

3.3. The structural model

The results of our study are shown in Figure 1, whichshows estimated path coefficients and the significanceof the path, which is indicated with an asterisk. Testsof significance were performed using the bootstrapprocedure. The findings indicate that all the hypothe-sised paths are significant. Standardised bs show thathabit (ß ¼ 0.304) and utilitarian value (ß ¼ 0.388)have a strong and significant influence on affect.Hedonic value (ß ¼ 0.187) also has a significantinfluence on affect. Social factors have a significantinfluence (ß ¼ 0.250) on habit. At the online usagelevel, intentions have a strong and significant influenceon behaviour (ß ¼ 0.443). Overall, the research modelaccounts for 19.6% (R2 ¼ 0.196) of the variance inbehaviour.

Hypothesis H1 stated that normative beliefs wouldhave a clear impact on habit. This hypothesis consistedof elements that described how the respondents werepersuaded by their friends, fellow students or collea-gues in their working environment. This is consistentwith the Triandis model, which stated that habit(learning) is a function of, amongst other things,reinforcement and social desirability (Triandis 1980).Our findings also confirm earlier research results, cf.Section 2.3. This interdependence does not shed lighton the basic and open question though, of whenrepeated actions become a habit. For example, in thise-commerce case, how many times must the individualfrequently visit the online site in order to make a habit

out of this behaviour? However, frequency is not theonly variable which may explain this problem (Ajzen2002). According to the data (Table 2), in e-commercethe frequency of visit to the actual site is low, but socialfactors have a quite strong effect on habit, thus othersituational beliefs (external factors) must also be usedto explain this phenomenon (Aarts et al. 1997).

Furthermore, hypothesis H2 was supported, de-scribing the strong impact of habit on affect. Thisinterdependence showed clearly how the use of onlineshopping sites had become a natural custom (habit) forthe respondents. In some cases it could even bedescribed as a compulsive and pathological behaviour(Morahan-Martin and Schumacher 2000).

Even though the term ‘affect’ ‘refers to theemotional system of an individual’ (Triandis 1980),hypothesis H3 was more strongly supported thanhypothesis H4. This rational side is explained withutilitarian values, such as the price being right, timebeing saved or overall satisfaction with the products orservices bought from an online shop. Hypothesis H4indicated that hedonic values reflect enjoyment-relatedelements in affect, such as online shopping absorbingthe shopper, online shopping also being a nice way tospend time, or the online shopper experiencing escapistemotions. Both rational and hedonic values areimportant factors influencing the online shoppingaffect, thus strengthening emotional and rationalattitudes towards e-commerce behaviour. From thecustomer’s viewpoint and that of their affect towardsan online shop, these values should appear in theconstruction and functionality of the web pages so thecustomer can have both kinds of experience, in orderto find the site valuable and further to patronise theonline shop. These findings are also consistent withearlier research, cf. Table 1.

4. Conclusion

In this research article, we have focused on e-com-merce customer behaviour towards online shoppingviewed from a habit and value perspective. We choosea subset of constructs and causal relationships fromTriandis’ Behavioural Framework (1980). Triandis’model explains individuals’ behaviour while takinginto consideration the individual’s habitual behaviourand the judgement of social peers on the individual’sown behaviour. We extended the Triandis model withutilitarian value and hedonic value constructs. InTriandis’ model, the independent variable habitexplains the individual’s actual behaviour. Our resultssuggest that the social factor is a preceding factor ofhabit and the social factor has an important affect onhabit formulation. Our results support the conjecturethat habit has an important role in e-commerce

Figure 1. The research model. * ¼ 0.05 level, ** ¼ 0.01level, *** ¼ 0.001 level.

customer behaviour. According to data, the Triandismodel refined with utilitarian and hedonic value, bothvalues play an important role in explaining individuals’e-commerce customer behaviour. Results also supportthe preconception that Triandis’ model is applicable inexplaining consumers’ online behaviour.

We found that normative beliefs have a significantimpact on habit and, further, that habit itself had astrong significant impact on affect. The former findingindicates that when the frequency of past behaviour isnot high, habit is determined by deliberate decision-making, in our case the recommendations or persua-sion of others. It could be that when the behaviour isnot repetitive, consumers are not familiar with onlineshopping; their behaviour is based on reasoning,taking into consideration others’ opinions or experi-ences relating to online shopping.

Our respondents were moderate online users, astheir online usage time per week and their weeklyonline frequency were not very high (Table 2).However, their computer expertise and readinesswere good, meaning that the respondents had a goodability to establish frequent usage behaviour.

Both the utilitarian and the hedonic values have asignificant impact on affect, and on e-commercebehaviour. This indicates that intrinsic motivationalmatters such as fun and enjoyment are importantissues that have an influence on consumer behaviour.When consumers are recreationally motivation-oriented, it has a positive affect on pleasantness andsales because recreational consumers appreciate richand engaging shopping experiences (Kaltcheva andWeitz 2006). According to Bloch et al. (1986), in aphysical store consumers enjoy the shopping experi-ence itself and browsing shopping catalogues withoutthe intention to buy. Furthermore, van der Heijden(2004, 696) states: ‘The objective of a utilitarianinformation system is to increase the user’s taskperformance while encouraging efficiency’. With thisobjective the user achieves productive use of thesystem. On the other hand, he continues: ‘Therefore,important tactics that developers employ are theinclusion of hedonic content, animated images, a focuson colours, sounds, and aesthetically appealing visuallayouts’. In this way, the online shopping siteencourages a prolonged stay on the site. This is alsosupported by Babin et al. (1994), as they found thathedonic customers just browse and play around in thewebsite without any real buying intentions.

The online shop has both to amuse and to benefitthe customer; thus when planning, designing andmaintaining the website for an online shop, theseattributes should be kept in mind. Considering thefindings of our research we would recommend thatonline service providers, in order to serve utilitarian

customers, should, for example, explicitly present thetotal costs related to the product or service at quite anearly stage of the shopping process, and provide clearproduct information concerning quality and warranty.A substantial proportion of the customers took time toreconsider when ordering, and for this reason wewould recommend that the retailers should encouragethe customer to make a decision in this final crucialmoment of the buying process. Furthermore, wesuggest that retailers should take care of thesecustomers, for example offering them informationabout up-coming products or services, or introducingtargeted special offers. Satisfying customers’ varyingexpectations is a stiff challenge for retailers. However,winning a new customer is more expensive than tryingto keep an existing customer relationship.

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