Age, gender and income: do theyreally moderate online shopping

behaviour?Blanca Hernandez, Julio Jimenez and M. Jose Martın

Department of Marketing and Business Studies, University of Zaragoza,Zaragoza, Spain

Abstract

Purpose – The objective of this paper is to analyse whether individuals’ socioeconomiccharacteristics – age, gender and income – influence their online shopping behaviour. Theindividuals analysed are experienced e-shoppers i.e. individuals who often make purchases on theinternet.

Design/methodology/approach – The technology acceptance model was broadened to includeprevious use of the internet and perceived self-efficacy. The perceptions and behaviour of e-shoppersare based on their own experiences. The information obtained has been tested using causal andmulti-sample analyses.

Findings – The results show that socioeconomic variables moderate neither the influence of previoususe of the internet nor the perceptions of e-commerce; in short, they do not condition the behaviour ofthe experienced e-shopper.

Practical implications – The results obtained help to determine that once individuals attain thestatus of experienced e-shoppers their behaviour is similar, independently of their socioeconomiccharacteristics. The internet has become a marketplace suitable for all ages and incomes and bothgenders, and thus the prejudices linked to the advisability of selling certain products should berevised.

Originality/value – Previous research related to the socioeconomic variables affecting e-commercehas been aimed at forecasting who is likely to make an initial online purchase. In contrast to themajority of existing studies, it is considered that the current development of the online environmentshould lead to analysis of a new kind of e-shopper (experienced purchaser), whose behaviour differsfrom that studied at the outset of this research field. The experience acquired with online shoppingnullifies the importance of socioeconomic characteristics.

Keywords Electronic commerce, Internet shopping, Age groups, Gender, Income, Spain

Paper type Research paper

IntroductionIn the last few decades extensive research has been conducted into informationtechnology (IT) adoption, testing a series of factors considered to be essential forimproved diffusion. Some studies analyse IT characteristics such as usefulness, ease ofuse and/or security (Davis, 1989; Yu et al., 2005), others focus on the emotions andexperiences of users (Agarwal and Prasad, 2000; Fiore and Kim, 2007) and a thirdgroup attempts to determine the importance of socioeconomic user characteristics,

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1468-4527.htm

The authors wish to express their gratitude for the financial support received from the SpanishGovernment CICYT (ECO 2008-04704), the Aragon Regional Government (Generes S-09; DGA138/08) and Catedra Telefonica of the University of Zaragoza (267-184).

Age, gender andincome

113

Refereed article received23 September 2009

Approved for publication4 May 2010

Online Information ReviewVol. 35 No. 1, 2011

pp. 113-133q Emerald Group Publishing Limited

1468-4527DOI 10.1108/14684521111113614

such as age, gender, educational level, place of residence and income. These lattercharacteristics have been commonly employed in the field of marketing for purposes ofmarket segmentation and may explain changes in the behaviour tested (Venkateshet al., 2003; Bigne et al., 2005). Their importance in the IT adoption process hasappeared to be unquestionable, and essential to understanding user behaviour.

However, it should be remembered that the objective of many such studies has beento analyse the adoption of a new IT, in order to explain why this occurred or to improvethe rate of penetration achieved. Furthermore, such research was usually performed atthe outset of the diffusion process, when the number of users was low or those usershad insufficient experience with the IT in question.

The situation is similar regarding e-commerce, where the majority of studies havetaken for granted the importance of including these variables when studyinge-commerce adoption, as these were believed to explain or forecast who buys or whowill buy on the internet. The rapid evolution of e-commerce in recent years has madeavailable to us sufficiently large samples of experienced e-shoppers i.e. individuals whooften make purchases on the internet. Such shoppers, who are already familiar with thecharacteristics of the new channel, display different behaviour to potential e-shoppers(Gefen et al., 2003). Consequently, the effect of some variables thought earlier to becrucial may have varied.

The objective of the present study is to test whether the socioeconomiccharacteristics of experienced e-shoppers – such as gender, income and age – reallymoderate the effect of their perceptions of online shopping behaviour. In contrast to themajority of existing research on IT, we consider that the current development of theonline environment and the experience acquired by individuals from previouse-purchases can attenuate or even nullify the effect of these characteristics.

To fulfil the above-mentioned objective we shall develop a broadened TechnologyAcceptance Model (TAM) by including variables such as previous use of the internet(acceptance, frequency of use and satisfaction) and the self-efficacy perceived by theonline shopper. The causal model will be estimated using structural equationmodelling techniques (SEM), followed by tests of the moderating effect ofsocioeconomic variables on perceptions and online shopping behaviour.

Literature reviewTechnology acceptance modelThe technology acceptance model proposed by Davis (1989) and Davis et al. (1989) isintended to explain the technological behaviour of users by examining the effect ofperceived ease of use (PEOU) and perceived usefulness (PU). The former refers to theperception that the employment of a technology does not require additional effort,while the latter reflects the degree to which a user considers that such employmentimproves his or her results (Davis, 1989). The version formulated by Davis et al. (1989)includes attitude, as the intermediary between explanatory perceptions and behaviour.Attitude is defined as the inclination or feeling which produces a predisposition to reactfavourably or unfavourably to a stimulus.

With regard to the final variables representing individuals’ behaviour, some studiesconcentrate on future intentions to use an IT (Liao et al., 2007; Tsai and Su, 2007), whileothers analyse the use already made (see for example Klopping and McKinney, 2004).Both variables are closely interrelated, since the use made determines the intentions of

OIR35,1

114

the individual, in the same way that intentions explain the subsequent use that will bemade; consequently, intentions may be employed to proxy behaviour. However, it isdifficult to determine the advantages of analysing one concept or the other, since theyeach reflect a nuance which must be taken into account when analysing onlineshopping behaviour. Thus, the model formulated in the present study includes bothcurrent online shopping behaviour and intentions to purchase in the future (seeGoldsmith, 2002; Van den Poel and Buckinx, 2005).

Self-efficacy and previous use of the internetThe development undergone by the TAM demonstrates the need to broaden its initialstructure by including other factors that permit the antecedents of perceived usefulnessand ease of use to be understood (Childers et al., 2001; Shih, 2004). The present studyhas included previous use of the internet and the self-efficacy perceived by the user asprior external variables (a term proposed by Davis et al., 1989), which precedeperceptions linked to e-commerce.

Self-efficacy reflects the beliefs of the individual with regard to his or her capacity toact in a specific way and to achieve the results desired (Bandura, 1977). Applied toe-commerce this concept means that the individual feels capable of searching forinformation and making purchases on the internet, and safe and comfortable duringthe interaction. The importance of this perception has been tested by distinct models ofbehaviour – Social Cognitive Theory (Bandura, 1977), the Theory of PlannedBehaviour (Schifter and Ajzen, 1985) and the Decomposed Theory of PlannedBehaviour (Taylor and Todd, 1995) – which have all demonstrated the effect ofself-efficacy upon the remaining perceptions of the individual and, therefore, upon hisor her final behaviour (Yi et al., 2006; Wu et al., 2007).

The previous use of the internet as an explanatory variable of behaviour hasacquired great influence. User perceptions of e-commerce are determined by theexperiences the user has had with the internet (Im et al., 2008). Miyazaki and Fernandez(2001) state that previous employment of the internet reduces the risk perceived byusers during online shopping, which in turn increases user satisfaction and encouragesrepeat behaviour i.e. future purchases. Thus, the evolution of e-commerce dependslargely on the acceptance and understanding of related ITs such as the internet byconsumers, and past adoption behaviour is a powerful and consistent predictor ofsubsequent behaviour (Bigne et al., 2008).

The present study analyses the effect of individuals’ previous use of the internet, onthe basis of the inclusion and measurement of three variables: acceptance of theinternet, frequency of use and satisfaction. Satisfaction has been included on the basisof Expectation-Confirmation Theory (Oliver, 1980). This variable refers to the“performance” which the individual considers to have obtained, reflects the successachieved during his or her past interactions and influences his or her subsequentbehaviour (Raymond, 1990; Soh et al., 1992). These three variables are closelyinterrelated, since the acceptance of the internet influences the use made of it (Lohseet al., 2000; Shih, 2004) as well as the satisfaction provided by past experiences(Gelderman, 1998; Bhattacherjee, 2001). User satisfaction causes both the initial IT andother similar technologies to be accepted (Soh et al., 1992), thereby altering attitude,intention and the intensity of use (Woodroof and Burg, 2003). As a result usersatisfaction with the internet affects the development of e-commerce, since individuals

Age, gender andincome

115

who search for information via the internet may experience satisfaction whichconditions their subsequent behaviour with regard to IT and online shopping (Shih,2004).

On the basis of these relationships we propose the model in Figure 1. A similarmodel has already been successfully tested (Hernandez et al., 2009).

Research hypotheses regarding the influence of socioeconomiccharacteristicsThe literature has considered the socioeconomic characteristics of individuals to be keyfactors in the analysis of their technological behaviour (Venkatesh and Morris, 2000;Venkatesh et al., 2003). In early studies in which the TAM was applied, authors such asAdams et al. (1992) encouraged the analysis of additional variables in an attempt tothereby complete the effect of user perceptions. Other authors, such as Agarwal andPrasad (1998), considered that the lack of such variables was one of the principalshortcomings of the model proposed by Davis (1989), while Venkatesh et al. (2003, p. 445)believe it necessary to include characteristics such as gender and age to complete theexplanatory capacity of the models analysed, since these modifying variables improvethe predictive capacity of the model beyond its original specification.

The majority of studies regarding such socioeconomic characteristics wereundertaken in the initial phases of development of the IT in question, when individualshad performed very few interactions with the online tools or medium under analysis.Consequently it was logical to assume that factors such as age, gender or incomeinfluenced their technological behaviour. However, in recent years the use of ITs suchas the internet and e-commerce has become widespread, especially in moretechnologically developed contexts and cultures, and thus it is reasonable to assumethat the importance of these accepted characteristics has also evolved.

We do not bring the generally accepted profile of online shoppers into question. Infact, we consider that socioeconomic characteristics do indeed affect the initial decisionwhether to make use of an IT, as has been demonstrated in the case of the internet (Liand Kirkup, 2007), email (Gefen and Straub, 1997), or m-commerce (Bigne et al., 2005).Nevertheless, it must be remembered that the behaviour of experienced users is notidentical to that of an individual during their initial employment of the IT in question(Gefen et al., 2003; Yu et al., 2005), since the experience acquired modifies the effect ofthe variables considered. Thus, we believe that once an individual becomes familiarwith the IT (in our case, e-commerce), the experience acquired may nullify theimportance of their socioeconomic characteristics (Sun and Zhang, 2006). To this end

Figure 1.Model of e-commerceacceptance

OIR35,1

116

authors such as Anandarajan et al. (2000) have already argued that variables such asgender and age are not correlated with the employment of the internet by experiencedusers in their workplace. For the case of e-commerce we shall test whether the fact ofmaking frequent online purchases means that the socioeconomic characteristics ofindividuals do not produce significant differences in their e-shopping behaviour.

We shall now separately examine each of the variables considered: age, gender andincome.

The moderating effect of ageA review of the traditional literature underlines the importance of users’ age in theanalysis of their behaviour (Harrison and Rainer, 1992; Hubona and Kennick, 1996). Inthe IT field some studies have considered that computer skills are more easily learned byyounger individuals (Czara et al., 1989; Hubona and Kennick, 1996). Furthermore,younger individuals usually possess greater experience with the internet, and aspectssuch as usefulness and attitude acquire greater importance for them, whilst older peopleperceive greater risks, have more difficulty in creating syntactically complex commandsand place more importance upon the perception of self-efficacy (Morris and Venkatesh,2000; Trocchia and Janda, 2000). Thus, some studies have included age as a relevantvariable in the explanation of online shopping behaviour (see for example Zhang, 2009).

Trocchia and Janda (2000) consider that the principal obstacles to the evolution ofe-commerce, which make older consumers more reluctant to shop online, are:

. lack of IT experience;

. resistance to change; and

. their insistence on trying out the product before purchase.

Age is positively associated with difficulty in processing stimuli (Morris andVenkatesh, 2000) and strongly correlated with the amount of time untrained users needto become familiar with computers (Gomez et al., 1986). Thus, Trocchia and Janda(2000) argue that older users’ lack of experience with the medium prevents them fromevaluating the advantages that the internet offers as a shopping channel, therebyhindering their participation.

Such research leads to questioning whether it is not age which impedes thefinalisation of an internet transaction, but rather the lack of user experience, which isgenerally manifested in older individuals. Thus, older individuals display lesswillingness to adopt the new channel, due to the distrust they have of the internet,which derives more from their lack of experience than from their age. If an olderindividual overcomes the barrier of the initial purchase, it is probable that theperception of the benefits obtained becomes more immediate and that his or herpurchasing behaviour is similar to that of any other purchaser, independently of age.

Furthermore, it must be remembered that the course of time alone means that theaverage age of e-shoppers is continuously rising, since individuals who at the end ofthe last century were 30 are 40 today. Consequently, life stages which would untilrecently have been considered to be far removed from more technologically-inclinedgenerations currently comprise individuals who may have been interacting with theinternet for several years and thus gained considerable familiarity. Moreover, userswho have replaced them (i.e. those who are 20-30 years old) possess a broad digitalculture.

Age, gender andincome

117

Some authors have found that age had no significant relationship with IT use,stating that simply assuming that young people already knew about the internet andthat older people were resistant was incorrect (Smith and Comstock, 1995; Zhang, 2005;Roussos, 2007). Al-Somali et al. (2009) analysed the use of e-banking for a sample ofexperienced clients, finding that age was not correlated with attitude and,consequently, did not significantly influence their behaviour. Finally, McCloskey(2006) concluded that age influences the initial decision regarding whether to purchaseon the internet, but not the subsequent behaviour of e-shoppers, such as the number oftransactions or the amount spent. Based on these findings we formulate the twofollowing hypotheses:

H1a. The effect of previous use of the internet upon the intention to makesubsequent purchases is not moderated by the age of the experiencede-shopper.

H1b. The effect of perceptions upon the intention to make subsequent purchases isnot moderated by the age of the experienced e-shopper.

The moderating effect of genderThe influence of gender upon decision-making and shopping behaviour has been asubject of special interest in the field of marketing. It has also been analysed withregard to the process of acceptance of new ITs, concluding that IT characteristics anduse are evaluated differently, depending on the gender of the individual (Gefen andStraub, 1997; Venkatesh and Morris, 2000). Sun and Zhang (2006) state that three traitsdetermine these differences:

(1) men are more pragmatic;

(2) women experience greater anxiety when faced with new activities; and

(3) women are more strongly influenced by their immediate environment.

These factors affect variables such as usefulness, ease of use, self-efficacy andsubjective norms (Venkatesh and Morris, 2000).

Despite these apparent differences derived from gender, recent surveys (Eurostat,2009) suggest that an increasing number of women use the internet and that the gendergap in this medium is decreasing. Moreover, recent research has found no statisticallysignificant differences between males and females with regard to internet use (Zhang,2005; Shin, 2009). One possible explanation of this similarity is to be found in classicstudies, which state that gender-related differences are only significant with regard toinitial expectations of the activity and do not affect the actual use (Deaux, 1984; Chen,1985; Venkatesh et al., 2000). Men and women display the same interest in computers,as long as they possess similar levels of experience (Chen, 1985). Thus differencesarising from gender are narrowed following the acquisition of specific technologicalexperience or the use of generally accepted ITs (Shashaani, 1997; Kirkpatrick andCuban, 1998; Wong and Hanafi, 2007).

A longitudinal analysis performed by Venkatesh et al. (2000) demonstrated thatmedium- and long-term decisions (i.e. decisions made following initial use) were notmoderated by user gender. In this regard authors such as Wong and Hanafi (2007) andAl-Somali et al. (2009) have demonstrated that gender-derived differences areextremely slight in a sample of individuals with prior experience of the IT under

OIR35,1

118

analysis. From a social constructionist perspective, “both gender and technologyprocesses . . . are shaped, or acted out, in interaction” (Silva, 2000, p. 613). In otherwords, both gender differences and IT change together with the societies they formpart of and together with users’ experience (Li and Kirkup, 2007).

We believe it is interesting to test whether gender-derived differences exist in thecase of experienced online shoppers, employing the following hypotheses:

H2a. The effect of previous use of the internet upon the intention to makesubsequent purchases is not moderated by the gender of the experiencede-shopper.

H2b. The effect of perceptions upon the intention to make subsequent purchases isnot moderated by the gender of the experienced e-shopper.

The moderating effect of incomeAs a variable that may encourage or prevent the use of e-commerce, income is anothercharacteristic that has attracted considerable research attention in the field oftechnology acceptance (Serenko et al., 2006; Allard et al., 2009; Shin, 2009). Severalstudies have included it as an explanatory variable of shopping behaviour, yet theresults concerning its significance are contradictory (Miyazaki and Fernandez, 2001;Raijas and Tuunainem, 2001; Lu et al., 2003; Al-Somali et al., 2009).

Higher income causes internet users to perceive lower implicit risks in undertakingonline purchases and thereby affects their demand for internet products and services.Low income discourages online transactions, and perceptions of self-efficacy, ease ofuse and usefulness should improve with rising incomes, due to the ability to withstandpossible financial losses. Usually, income is reflected in the professional status or socialclass of the individual – different professional categories are accompanied by differentincomes and by different levels of IT knowledge and experience. Thus, such categoriesmay produce different user attitudes and behaviour regarding information systems(Hubona and Kennick, 1996; Chau and Hu, 2002).

The internet is a channel open to all, independently of their social class orpurchasing, and although in its initial stages there was a clear bias in the profile of itsusers, produced by income, falling prices of computers and internet connections meanthat access is currently affordable for the majority of the population. Furthermore,many users declare that one of the advantages of the internet is that it allows them topurchase the same products as offline, but more cheaply. Consequently, in recent yearsthe internet has become more appealing to the general public, offering attractivealternatives for more price-conscious consumers.

We consider that user income has an effect on the first contact with the internet ande-commerce since, as previous research has demonstrated, people with high incomesperceive less risk in the adoption of new ITs (Hubona and Kennick, 1996; Lu et al.,2003). However, once users have acquired experience their technological behaviour isno longer influenced by their income. Therefore income does not have a significanteffect upon the perceptions, attitude and behaviour of experienced users of an IT (seeAl-Somali et al., 2009 for e-banking). The present study shares this notion, and thus ithas established that all experienced e-shoppers display similar online purchasingbehaviour, independently of their incomes. Consequently we formulate the followinghypotheses:

Age, gender andincome

119

H3a. The effect of previous use of the internet upon the intention to makesubsequent purchases is not moderated by the income of the experiencede-shopper.

H3b. The effect of perceptions upon the intention to make subsequent purchases isnot moderated by the income of the experienced e-shopper.

Research designData collectionThe data were obtained in Spain through a survey performed using thecomputer-assisted telephone interviewing technique. A pre-test was carried out tocorrect possible defects and to anticipate interviewees’ doubts and problems during thedata collection process.

In order to guarantee the representativeness of the population, the random quotasampling method was employed, according to criteria of age, gender and geographicallocation. To this end the principal national Spanish telephone directory was used toselect numbers at random. This telephone directory contains national users’ names andlandline telephone numbers, ordered by geographical area. It is compiled periodicallyby the leading operator, and former monopoly of landline telephones in Spain,Telefonica, SA, which, at the time of the study, owned and rented out (both to finalclients and to wholesalers) over 95 per cent of existing lines. Furthermore, in order totake into account the 5 per cent of users who have cable telephones, and are nottherefore included in Telefonica’s directory, the online QDQ directory (www.qdq.com)was also searched at random.

A total of 2,615 telephone calls were made and 225 interviewees were considered tobe experienced e-shoppers, i.e. individuals who often make purchases on the internet.The profile obtained of experienced e-shoppers (see Table I) is similar to that obtainedby the most prestigious studies of e-commerce undertaken at a national level (AECE,2009).

The questionnaire began with a filter question, in order to select those individualswho make frequent online purchases and who fulfil the necessary condition toparticipate in the survey. Subsequently, a series of indicators was included to measurethe factors contained in the model. The majority of these were measured by seven-pointLikert-type scales and adapted on the basis of past research in the TAM field; the soleexception was the “current online shopping behaviour” factor. The items included inthe survey are listed in Table II.

Data analysisThe next step was to undertake the analyses required to filter the measurementscales and guarantee their suitability. First exploratory studies were performed toensure their reliability. We eliminated those indicators which displayed anitem-total correlation of below 0.3 (Nurosis, 1993) and those whose Cronbach’salpha did not exceed the reference value of 0.7. On the basis of these premises weascertained that the item ACCE2 did not fall within the recommended limits, andit was therefore eliminated. Following this initial filtering, satisfactory results wereobtained.

The second phase of scale validation consisted of performing a confirmatoryfactor analysis (Hair et al., 1999) (Table III). To this end the structural equation

OIR35,1

120

method (SEM) was applied, using EQS 6.1 statistical software and employing therobust maximum likelihood estimation method, since our data did not fulfil thehypothesis of normality (Bentler, 1995). Reliability, initially measured by Cronbach’salpha, was verified by the Composite Reliability Coefficient (CRC) ( Joreskog, 1971).All the factors attained the recommended limit of 0.6 (Bagozzi and Yi, 1988). Withregard to validity, a distinction was made between convergent and discriminantvalidity. The former tests the convergence between the items and theircorresponding construct; values for the standardised loadings must be higherthan 0.5, with a significance level of 0.01 (Steenkamp and Van Trijp, 1991) andpresent R 2 higher than 0.5 ( Joreskog and Sorbom, 1993). To test discriminantvalidity the confidence interval among different factors was calculated and it wasverified that the value of 1 was not included in any of them (Table III). On the basisof these criteria we conclude that our measures exhibit sufficient evidence ofreliability and convergent and discriminant validity.

Decriptor

Educational levelNo studies 0Primary education 4.3Secondary education 49.3Technical diploma 18.8University degree 24.6Doctorate 2.9

JobStudent 13.1Employee 50.7Freelance worker 30.4Unemployed/retired 4.4Housewife 1.4

HabitatRural 15.9Urban 84.1

GenderMale 76.8Female 23.2

Age15-24 14.525-34 39.135-44 2945-54 13.1Over 55 4.4

Income (e/year)None 15Less than 8,400 58,400 to 16,800 3516,800 to 25,200 23.33More than 25,200 21.67

Table I.Description of the sample(experienced e-shoppers)

Age, gender andincome

121

Factor Indicators

Acceptance of the internetDavis (1989)Davis et al. (1989)

My general opinion of the internet ispositive

ACCE1

Using the internet is easy for me ACCE2Using the internet seems useful to me ACCE3

Frequency of use How often do you access the internet? FREQ

Satisfaction with the internetSpreng et al. (1996), Bhattacherjee(2001)

The experience I have had with the internethas been satisfactory

SATIS1

In general, I am satisfied with the serviceprovided by the internet

SATIS2

Perceived self-efficacyKoufaris (2002), Vijayasarathy (2004)

I feel capable of buying a product on theinternet

PSE1

I feel capable of finding shopping sites onthe internet

PSE2

I feel comfortable looking for informationabout a product on the internet

PSE3

Perceived ease of useDavies (1989), Davis et al. (1989),Taylor and Todd (1995), Yu et al. (2005)

Learning to use the internet to buy a productwould be easy for me, even for the first time

PEOU1

Using the internet to buy a product wouldbe easy to do for me

PEOU2

The internet would be easy to be use to domy shopping

PEOU3

Perceived usefulnessDavis (1989), Davis et al. (1989), Taylorand Todd (1995), Yu et al. (2005)

Using the internet to acquire a productwould allow me to shop more efficiently

PU1

Using the internet to acquire a productwould allow me to do my shopping morequickly

PU2

Using the internet to acquire a productwould be useful to do my shopping

PU3

Online shopping attitudeDavis et al. (1989), Taylor and Todd(1995), Yu et al. (2005)

Using the internet to do my shopping is agood idea

ATT1

My general opinion of electronic commerceis positive

ATT2

Using the internet to purchase a productseems an intelligent idea to me

ATT3

Current online shopping behaviourHubona and Kennick (1996), Kloppingand McKinney (2004)

Number of purchases made by e-shopper CURR

Future online shopping behaviourTaylor and Todd (1995), Moon andKim (2001)

I will probably buy a product on the internet(soon)

FUT1

The internet will probably be the medium Iuse to do my shopping in the future

FUT2

I intend to use the internet to buy a productsoon

FUT3Table II.Measurement scales forquestionnaire variables

OIR35,1

122

ResultsThe next step was to analyse the causal relationships proposed in the model, usingstructural equation modelling. The goodness of fit indices was calculated and it wasverified that they attained the limits recommended by Hair et al. (1999) (Figure 2). Themajority of the relationships proposed are significant, while R1, R5 and R11 arerejected.

It is evident that future online shopping behaviour is determined by currentbehaviour and by the attitude of e-shoppers towards the online channel. Consequently,R10 and R13 are fulfilled. PU explains the attitude of the purchaser and current onlineshopping behaviour (R9 and R12), while PEOU does not have a significant effect uponcurrent online shopping (R11), although it does upon the attitude of the purchaser (R8).

CRC Factors Interval Factors Interval

ACCE 0.692 ACCE–SATIS (0.62–0.93) PSE–PU (0.61–0.84)ACCE–PSE (0.68–0.98) PSE–ATT (0.59–0.81)

SATIS 0.787 ACCE–PEOU (0.27–0.62) PSE–FUT (0.33–0.62)ACCE–PU (0.48–0.83) PSE–FREQ (0.11–0.45)

PSE 0.765 ACCE–ATT (0.45–0.79) PEOU–PU (0.33–0.60)ACCE–FUT (0.13–0.51) PEOU–ATT (0.39–0.64

PEOU 0.889 ACCE–FREQ (0.01–0.38) PEOU–FUT (0.22–0.50)SATIS–PSE (0.36–0.68 PEOU–FREQ (0.14–0.46)

PU 0.854 SATIS–PEOU (0.17–0.54) PU–ATT (0.88–0.98)SATIS–PU (0.13–0.56 PU–FUT (0.52–0.74)

ATT 0.906 SATIS–ATT (0.18–0.59) PU–FREQ (0.00–0.31)SATIS–FUT (0.17–0.49 ATT–FUT (0.52–0.75

FUT 0.860 SATIS–FREQ (20.17–0.14) ATT–FREQ (20.09–0.20)PSE–PEOU (0.58–0.82) FUT–FREQ (0.08–0.38)

Notes: Absolute fit indices GFI ¼ 0.89; RMSR ¼ 0.06; RMSEA ¼ 0.067. Incremental fit indicesNNFI ¼ 0.91; IFI ¼ 0.93; CFI robust ¼ 0.96. Parsimony fit indices x 2/g.l. ¼ 2.00

Table III.Confirmatory factor

analysis

Figure 2.Results obtained for the

proposed model

Age, gender andincome

123

The greater the perceived usefulness, the greater the number of exchanges completed,and the more positive is the attitude towards online shopping.

We should underline the important role of self-efficacy as an antecedent of theperceptions linked to online shopping (ease of use and usefulness); R6 and R7 areverified. Self-efficacy is explained by the previous use of the internet, measured byacceptance ðß3 ¼ 0:87Þ and frequency of use ðß4 ¼ 0:18Þ: R3 and R4 are confirmed.However, satisfaction with the internet does not influence perceived self-efficacy, andconsequently R5 is rejected. Acceptance of the internet does not affect frequency of use,but it does influence satisfaction with the internet ðß2 ¼ 0:74Þ: R1 is rejected and R2 isconfirmed. Thus, shoppers who more readily accept the internet and who use itfrequently perceive greater self-efficacy during online shopping.

Having tested the causal model, the next stage was to estimate it by dividing thetotal sample according to the values that the socioeconomic variables may take (maleand female, young, adult and senior and low and high income users). We subsequentlyperformed multi-sample analyses, in order to discover whether the relationshipsformulated for these variables were statistically the same.

With regard to the moderating effect of age (H1), this variable was divided intothree intervals, to analyse its effect upon online shopping behaviour. Three populationsub-samples were considered: 15-25 years old, or those who have grown up with theinternet (called “junior”); 26-45, or those adopting the internet at an early age (“adult”);and over 46, who adopted the internet when already mature (“senior”). The divisioninto age groups was established on the basis of similar national (e.g. AECE, 2009) andinternational studies (Office for National Statistics, 2009, in the UK), aggregating thecategories of older purchasers (45 þ ). This aggregation is due to the low number ofpurchasers aged over 55 in the overall population in Spain (5 per cent) and in oursample in particular (4.4 per cent).

The results demonstrate that the multi-sample model obtains a good fit and that allthe relationships behave similarly, independently of the age of e-shoppers (Table IV).There are no differences between the effects of previous use of the internet and those ofthe perceptions of e-commerce upon purchasing behaviour. Consequently, H1a andH1b are supported. It is probable that once the initial barriers to e-commerce have beensurmounted, the age of e-shoppers does not have a significant effect upon theirbehaviour.

With regard to the moderating effect of gender (H2) the statistics of the modelexceed the recommended levels, and thus the fit of the multi-sample model is correct(Table V).

Those factors related to the internet (acceptance, frequency and satisfaction) arestable for men and women (R1 to R5). Consequently, the moderating effect of genderupon the relationships between previous use of the internet and online shoppingbehaviour disappears when a sample of experienced e-shoppers is analysed. H2a issupported.

The only significant difference obtained is apparent in the relationship“attitude-future intention” (R10). Women’s attitudes influence their future intentionsless than in the case of men i.e. the fact of having purchased via the internet leads maleshoppers to display greater intentions to shop again in the future. In the case of “ease ofuse-current online shopping behaviour” (R11), despite differences existing betweenmen and women, the relationship in the two sub-samples is not significant. Therefore,

OIR35,1

124

Jun

ior

Ad

ult

Sen

ior

J-A

J-S

A-S

LM

Dif

.aL

MD

if.a

LM

Dif

.a

R1:

AC

CE

-FR

EQ

0.31

6*

*0.

057

0.17

30.

781

NO

0.06

8N

O0.

242

NO

R2:

AC

CE

-SA

TIS

0.30

8*

0.70

4*

**

0.79

0*

**

0.28

6N

O2.

985

*Y

ES

1.27

4N

OR

3:A

CC

E-P

SE

0.41

4*

*1.

017

**

0.03

61.

291

NO

1.32

4N

O1.

337

NO

R4:

FR

EQ

-PS

E0.

305

0.17

3*

0.12

80.

060

NO

0.21

3N

O0.

079

NO

R5:

SA

TIS

-PS

E0.

225

20.

272

0.57

40.

576

NO

1.29

8N

O1.

331

NO

R6:

PS

E-P

EO

U0.

539

**

0.51

8*

**

1.18

90.

247

NO

0.95

4N

O0.

966

NO

R7:

PS

E-P

U0.

761

**

*0.

861

**

*0.

801

**

0.77

6N

O1.

111

NO

1.17

2N

OR

8:P

EO

U-A

TT

0.09

40.

214

**

0.08

00.

553

NO

0.11

7N

O0.

225

NO

R9:

PU

-AT

T0.

952

**

*0.

805

**

*0.

782

**

*0.

021

NO

0.23

8N

O0.

090

NO

R10

:A

TT

-FU

TU

RE

0.67

6*

**

0.83

3*

**

0.97

2*

**

0.02

7N

O0.

142

NO

0.08

2N

OR

11:

PE

OU

-CU

RR

EN

T0.

449

20.

091

20.

341

0.16

9N

O1.

264

NO

0.81

1N

OR

12:

PU

-CU

RR

EN

T0.

081

0.41

8*

**

0.40

3*

*0.

179

NO

0.00

6N

O0.

136

NO

R13

:C

UR

RE

NT

-FU

TU

RE

0.59

9*

**

0.33

1*

**

0.37

31.

157

NO

0.06

2N

O0.

017

NO

Notes:

aS

ign

ifica

nt

dif

fere

nce

;* p

,0.

1;*

* p,

0.05

;*

** p

,0.

01.

Goo

dn

ess

offi

tin

dic

es(m

ult

i-sa

mp

le)

GF

I¼

0.77

3;R

MS

EA¼

0.07

0;IF

I¼

0.85

7;N

NF

I¼

0.83

4;C

FI¼

0.85

4;x

2/

g.l.¼

1.80

Table IV.Analysis of the

moderating effect of age

Age, gender andincome

125

PEOU does not significantly affect their behaviour and it cannot be affirmed that a realdifference between men and women exists. The remaining perceptions influence menand women in the same way, and the conclusion reached is that the existence of a soledifference between the two genders means that H2b cannot be rejected.

Last, the majority of the differences derived from the moderating effect of income(H3) do not display significant values (Table VI).

The influence of previous use of the internet upon self-efficacy (and thus uponPEOU, PU and online shopping behaviour) is statistically identical for all experiencede-shoppers, regardless of their income. The sole relationship which varies according toindividual income is “ease of use-attitude” (R8). Lower-income consumers base theirattitude upon the ease of use they have perceived while performing online transactions.Consequently, PEOU acts as a significant influence in moulding their attitude

Females Males LM test Dif.a

R1: ACCE-FREQ 0.095 0.177 * * 0.627 NOR2: ACCE-SATIS 0.693 * * * 0.642 * * * 0.078 NOR3: ACCE-PSE 1.205 * * * 0.589 * * 1.135 NOR4: FREQ-PSE 0.079 0.199 0.538 NOR5: SATIS-PSE 20.266 0.072 0.686 NOR6: PSE-PEOU 0.388 * * * 0.600 * * * 0.365 NOR7: PSE-PU 0.743 * * * 0.901 * * * 0.273 NOR8: PEOU-ATT 0.253 * * 20.026 2.140 NOR9: PU-ATT 0.802 * * * 0.924 * * * 0.731 NO

R10: ATT-FUTURE 0.617 * * * 1.165 * * * 7.35 * * * YESR11: PEOU-CURRENT 20.171 0.102 2.935 * NOR12: PU-CURRENT 0.291 * * 0.440 * * * 1.068 NOR13: CURRENT-FUTURE 0.636 * * * 0.246 * * * 1.698 NO

Notes: a Significant difference; *p , 0.1; * *p , 0.05; * * *p , 0.01; Goodness of fit indices (multi-sample) GFI ¼ 0.800; RMSEA ¼ 0.060; IFI ¼ 0.887; NNFI ¼ 0.869; CFI ¼ 0.885; x 2/ g.l. ¼ 1.8

Table V.Analysis of themoderating effect ofgender

Higher Lower LM Test Dif.a

R1: ACCE-FREQ 20.004 0.150 0.749 NOR2: ACCE-SATIS 0.697 * * * 0.629 * * * 0.275 NOR3: ACCE-PSE 0.763 0.900 * 0.001 NOR4: FREQ-PSE 0.215 * 0.187 0.002 NOR5: SATIS-PSE 20.147 20.290 0.019 NOR6: PSE-PEOU 0.665 * * * 0.602 * * * 0.117 NOR7: PSE-PU 0.940 * * * 0.792 * * * 0.371 NOR8: PEOU-ATT 20.027 0.347 * * 4.43 * * YESR9: PU-ATT 0.832 * * * 0.697 * * * 0.226 NO

R10: ATT-FUTURE 1.107 * * * 0.828 * * * 0.631 NOR11: PEOU-CURRENT 0.003 20.069 0.266 NOR12: PU-CURRENT 0.423 * * * 0.384 * * 0.164 NOR13: CURRENT-FUTURE 0.440 * * * 0.532 * * * 0.114 NO

Notes: a Significant difference; *p , 0.1; * *p , 0.05; * * *p , 0.01; Goodness of fit indices (multi-sample) GFI ¼ 0.879; RMSEA ¼ 0.059; IFI ¼ 0.889; NNFI ¼ 0.871; CFI ¼ 0.887; x 2/ g.l. ¼ 1.66

Table VI.Analysis of themoderating effect ofincome

OIR35,1

126

regarding the new channel and in making future transactions. In contrasthigher-income shoppers do not appear to be influenced by ease of use whenevaluating e-commerce. The existence of only one difference in the 13 relationshipstested signifies that H3a and H3b are supported.

DiscussionThe objective of this study has been to demonstrate that, in contrast to the majorityof research into e-commerce acceptance, the socioeconomic characteristics of theindividual (age, gender and income) have scarcely any significance in theexplanation of the behaviour of e-shoppers, once these have acquired experiencewith the channel. We have formulated an extension of the TAM to include previoususe of the internet and the self-efficacy perceived by the individual with regard tothe online shopping process. The results obtained have permitted us to verify thehypotheses posed and affirm that the socioeconomic variables moderate neither theinfluence of previous use of the internet nor the perceptions of e-commerce; in shortthey do not condition the behaviour of the experienced shopper. Subsequently,socioeconomic characteristics as moderating variables, which until now have beenconsidered indisputable, become questionable once the user has acquired experiencewith the IT analysed.

We must clarify that these results do not signify that the number of experiencede-shoppers is identical between the junior and senior population segments, betweendifferent income levels, or between men and women, whether in absolute terms (totalnumber of shoppers) or relative terms (the number of people in this populationsegment). In fact, these characteristics continue to influence the capacity of theindividual to overcome the initial barriers inherent in e-commerce, as shown by thestatistics for adoption. Consequently,young well-off males are most likely to becomeexperienced e-shoppers; this explains the existence of percentage differences for thesecharacteristics in the sample analysed (see Table I). Nevertheless, once individualsattain the status of experienced e-shoppers, their behaviour is similar, independently oftheir age, gender or income level. This is probably due to the experience acquiredduring purchases modifying the effect exercised by these variables.

We do not question the validity of the generally accepted purchaser profile. Weestablish that, for experienced e-shoppers, socioeconomic variables do not give rise todifferences in the effect of their perceptions upon their online purchasing behaviour.This behaviour may depend upon other more complex variables, such as personality,lifestyle and perceptions of IT.

Conclusions and implicationsThe current diffusion of the internet in western countries is far removed from thecontext which shaped studies in the 1990s; consequently, academic research mustconstantly be adapted to its existing level of development and acceptance. In contrastto the majority of research on this subject, we consider that the current development ofthe online environment and the experience acquired by individuals with the electronicshopping channel attenuate or nullify the importance of their socioeconomiccharacteristics as explanatory variables of their behaviour. Therefore, the principalconclusion of our study is that socioeconomic variables, traditionally considered to beimportant, have ceased to be discriminant.

Age, gender andincome

127

The experience acquired by individuals during the online purchasing processcauses their behaviour to evolve and initially significant variables cease to be so whenit comes to making repurchasing decisions. Thus, when defining target markets,e-businesses must turn their attention away from socioeconomic variables and focus onother behavioural factors. It is possible that in moments prior to the use of atechnology, or even in the initial stages of its evolution, population segments orminorities apparently disadvantaged in the use of IT (women, senior citizens andlower-income individuals), may state that “we can but I cannot use IT” (Durndell et al.,1995). However, as they acquire experience with the technology and understand itsfunctioning, this belief disappears and the statement then becomes “I can use ITbecause I know how to do it” (Durndell et al., 1995).

We would like to underline that variables such as trialability – included by Rogersin his Diffusion of Innovations Theory (Rogers, 1995) – would permit the reluctance ofnon-shopping users to be eliminated, thereby facilitating the performance of onlinepurchases. Trialability would provide the necessary experience to continue shoppingvia the internet, homogenise shopper behaviour and nullify the effect of characteristicssuch as gender, age or income.

Another interesting finding of the present study is that older adults are activeparticipants in e-commerce. Although in the phases prior to the first purchasedifficulties are encountered with regard to the employment of ITs, once olderindividuals become familiar with e-shopping and have performed one or more onlinetransactions, their perceptions, attitudes and behaviour may not diverge from otherusers. This population segment represents a lucrative market; older people have lowdebt, high disposable income and additional leisure time (McCloskey, 2006). It shouldbe emphasised that although today this segment may still be a niche market, thepresence of such consumers on the internet will progressively increase, as the alreadydigitalised generations grow older. Thus, the internet has become a market suitable forall ages, and assumptions regarding the advisability of selling certain products shouldbe revised. New e-businesses directed at consumers hitherto practically ignored ine-commerce may emulate the success of those aimed principally at populationsegments with a higher percentage of e-shoppers in recent years (purchasers of music,software, etc.).

Some limitations of this study should be noted, as these suggest possible directionsfor future research. First, we should underline the need to perform a longitudinalanalysis which would permit us to understand the evolution over time of the variablestested. Second, it must be remembered that this study was undertaken in a developedcountry possessing technological experience and knowledge. However, in developingcountries where average education levels are lower, socioeconomic variables can beexpected to have a moderating effect upon IT behaviour. This is due to the continuedexistence of barriers which prevent older and poorer people from using IT (Maldifassiand Canessa, 2009). Third, the study of age could vary if a greater number of groupswere to be established. However, the low number of purchasers aged over 55 in thepopulation analysed prevented the creation of a fourth group. Finally, in forthcomingstudies we would like to contrast the effect of other moderating characteristics relatedto the type of IT analysed (individual or group, complexity, etc.).

OIR35,1

128

References

Adams, D.A., Nelson, R.R. and Todd, P.A. (1992), “Perceived usefulness, ease of use and usage ofinformation technology: a replication”, MIS Quarterly, Vol. 16 No. 2, pp. 227-47.

AECE (2009), Estudio sobre Comercio Electronico B2C 2009, available at: www.red.es/media/registrados/2009-10/1256816746333.pdf?aceptacion¼8686d2aacf93732ad9c39ce7ba5f0018(accessed 7 October 2010).

Agarwal, R. and Prasad, J. (1998), “The antecedents and consequents of user perceptions ininformation technology adoption”, Decision Support Systems, Vol. 22 No. 1, pp. 15-29.

Agarwal, R. and Prasad, J. (2000), “A field study of the adoption of software process innovationsby information systems professionals”, IEEE Transactions on Engineering Management,Vol. 47 No. 1, pp. 295-308.

Allard, T., Banin, B. and Chebat, J. (2009), “When income matters: customers’ evaluation ofshopping malls’ hedonic and utilitarian orientations”, Journal of Retailing and ConsumerServices, Vol. 16 No. 1, pp. 40-9.

Al-Somali, S., Gholami, R. and Clegg, B. (2009), “An investigation into the acceptance of onlinebanking in Saudi Arabia”, Technovation, Vol. 29 No. 2, pp. 130-41.

Anandarajan, M., Simmers, C. and Igbaria, M. (2000), “An exploratory investigation of theantecedents and impact of internet usage: an individual perspective”, Behavior andInformation Technology, Vol. 19 No. 1, pp. 69-85.

Bagozzi, R.P. and Yi, P.R. (1988), “On the evaluation of structural equation models”, Academy ofMarketing Science, Vol. 16 No. 1, pp. 74-94.

Bandura, A. (1977), Social Learning Theory, Prentice-Hall, Englewood Cliffs, NJ.

Bentler, P.M. (1995), EQS Structural Equations Program Manual, Multivariate Software, Encino,CA.

Bhattacherjee, A. (2001), “An empirical analysis of the antecedents of electronic commerceservice continuance”, Decision Support Systems, Vol. 32 No. 2, pp. 201-14.

Bigne, E., Ruiz, C. and Sanz, S. (2005), “The impact of internet user shopping patterns anddemographics on consumer mobile buying behaviour”, Journal of Electronic CommerceResearch, Vol. 6 No. 3, pp. 193-209.

Bigne, E., Ruiz, C., Aldas, J. and Sanz, S. (2008), “Influence of online shopping informationdependency and innovativeness on internet shopping adoption”, Online InformationReview, Vol. 32 No. 5, pp. 648-67.

Chau, P.Y.K. and Hu, J.H. (2002), “Investigating healthcare professionals’ decisions to accepttelemedicine technology: an empirical test of competing theories”, Information &Management, Vol. 39 No. 4, pp. 297-311.

Chen, S. (1985), “Gender and computers: the beneficial effects of experience on attitudes”, paperpresented at the 1985 Conference on Computers and Children, Ann Arbor, MI, 17-19October.

Childers, T.L., Carr, C.L., Peck, J. and Carson, S. (2001), “Hedonic and utilitarian motivations foronline retail shopping behavior”, Journal of Retailing, Vol. 77 No. 4, pp. 511-35.

Czara, S.J., Hammond, K., Blascovich, J.J. and Swede, H. (1989), “Age related differences inlearning to use a text-editing system”, Behavior and Information Technology, Vol. 8 No. 4,pp. 309-19.

Davis, F.D. (1989), “Perceived usefulness, perceived ease of use and user acceptance ofinformation technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-39.

Age, gender andincome

129

Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: acomparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1002.

Deaux, K. (1984), “From individual differences to social categories: analysis of a decade’sresearch on gender”, American Psychologist, Vol. 39 No. 2, pp. 105-16.

Durndell, A., Glissov, P. and Siann, G. (1995), “Gender and computing: persisting differences”,Educational Research, Vol. 37 No. 3, pp. 219-27.

Eurostat (2009), “Internet usage in 2009 – households and individuals”, available at: http://eeurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-QA-09-046/EN/KS-QA-09-046-EN.PDF(accessed 15 October 2010).

Fiore, A.M. and Kim, J. (2007), “An integrative framework capturing experiential and utilitarianshopping experience”, International Journal of Retail & Distribution Management, Vol. 35No. 6, pp. 421-42.

Gefen, D. and Straub, D.W. (1997), “Gender differences in the perception and use of e-mail:an extension to the technology acceptance model”, MIS Quarterly, Vol. 21 No. 4,pp. 389-400.

Gefen, D., Karahanna, E. and Straub, D.W. (2003), “Inexperience and experience with onlinestores: the importance of TAM and trust”, IEEE Transactions on EngineeringManagement, Vol. 50 No. 3, pp. 307-21.

Gelderman, M. (1998), “The relation between user satisfaction, usage of information systems andperformance”, Information & Management, Vol. 34 No. 1, pp. 11-18.

Goldsmith, R.E. (2002), “Explaining and predicting consumer intention to purchase over theinternet: an exploratory study”, Journal of Marketing Theory and Practice, Vol. 10 No. 2,pp. 22-8.

Gomez, L.M., Egan, D.E. and Bowers, C. (1986), “Learning to use a text editor: some learnercharacteristics that predict success”, Human-Computer Interaction, Vol. 2 No. 1, pp. 1-23.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1999), Multivariate Data Analysis,Prentice-Hall, Upper Saddle River, NJ.

Harrison, A.W. and Rainer, R.K. (1992), “The influence of individual differences on skill inend-user computing”, Journal of Management Information Systems, Vol. 9 No. 1,pp. 93-111.

Hernandez, B., Jimenez, J. and Martin, M.J. (2009), “The impact of self-efficacy, ease of use andusefulness on e-purchasing: an analysis of experienced e-shoppers”, Interacting withComputers, Vol. 21 Nos 1-2, pp. 146-56.

Hubona, G.S. and Kennick, E. (1996), “The impact of external variables on informationtechnology usage behavior”, IEEE Proceedings of the Hawaii International Conference onSystem Sciences, Vol. 4, IEEE Press, Los Alamitos, CA, pp. 166-75.

Im, I., Kim, Y. and Han, H.-Y. (2008), “The effects of perceived risk and technology type on users’acceptance of technologies”, Information & Management, Vol. 45 No. 1, pp. 1-9.

Joreskog, K. (1971), “Statistical analysis of sets of congeneric tests”, Psychometrika, Vol. 36 No. 2,pp. 109-33.

Joreskog, K. and Sorbom, D. (1993), LISREL 8 Structural Equation Modelling with the SimplisCommand Language, Scientific Software International, Chicago, IL.

Kirkpatrick, H. and Cuban, L. (1998), “What the research says about gender differences in access,use, attitudes and achievement with computers”, Educational Technology, Vol. 38 No. 4,pp. 56-61.

OIR35,1

130

Klopping, I. and McKinney, E. (2004), “Extending the technology acceptance model and thetask-technology fit model to consumer e-commerce”, Information Technology, Learningand Performance Journal, Vol. 22 No. 1, pp. 35-48.

Koufaris, M. (2002), “Applying the technology acceptance model and flow theory to onlineconsumer behaviour”, Information Systems Research, Vol. 13 No. 2, pp. 205-23.

Li, N. and Kirkup, G. (2007), “Gender and cultural differences in internet use: a study of Chinaand UK”, Computers & Education, Vol. 48 No. 2, pp. 301-17.

Liao, C.-H., Tsou, C.-W. and Huang, M.-F. (2007), “Factors influencing the usage of 3G mobileservices in Taiwan”, Online Information Review, Vol. 31 No. 6, pp. 759-74.

Lohse, G.L., Bellman, S. and Johnson, E.J. (2000), “Consumer buying behavior on the internet:findings from panel data”, Journal of Interactive Marketing, Vol. 4 No. 1, pp. 15-29.

Lu, J., Yu, C.S., Liu, C. and Yao, J. (2003), “Technology acceptance model for wireless internet”,Internet Research: Electronic Networking Applications and Policy, Vol. 13 No. 3, pp. 206-22.

McCloskey, D. (2006), “The importance of ease of use, usefulness, and trust to online consumers:an examination of the technology acceptance model with older consumers”, Journal ofOrganizational and End User Computing, Vol. 18 No. 3, pp. 47-65.

Maldifassi, J. and Canessa, E. (2009), “Information technology in Chile: how perceptions and useare related to age, gender and social class”, Technology in Society, Vol. 31 No. 3, pp. 273-86.

Miyazaki, A.D. and Fernandez, A. (2001), “Consumer perceptions of privacy and security risksfor online shopping”, The Journal of Consumer Affairs, Vol. 35 No. 1, pp. 27-44.

Moon, J.W. and Kim, Y.G. (2001), “Extending the TAM for a world wide web context”,Information & Management, Vol. 38 No. 4, pp. 217-30.

Morris, M.G. and Venkatesh, V. (2000), “Age differences in technology adoption decisions:implications for a changing work force”, Personnel Psychology, Vol. 53 No. 2, pp. 375-403.

Nurosis, M. (1993), Statistical Data Analysis, SPSS, New York, NY.

Office for National Statistics (2009), Internet Access Statistical Bulletin, London.

Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of satisfactiondecisions”, Journal of Marketing Research, Vol. 17, pp. 460-9.

Raijas, A. and Tuunainen, V.K. (2001), “Critical factors in electronic grocery shopping”,International Journal of Retail, Distribution and Consumer Research, Vol. 11 No. 3,pp. 255-65.

Raymond, G.A. (1990), “Problems and prospects in the study of international norms”,The International Studies Review, Vol. 41 No. 2, pp. 205-45.

Rogers, E.M. (1995), Diffusion of Innovations, Free Press, New York, NY.

Roussos, P. (2007), “The Greek computer attitude scale: construction and assessment ofpsychometric properties”, Computers in Human Behavior, Vol. 23 No. 1, pp. 578-90.

Schifter, D.B. and Ajzen, I. (1985), “Intention, perceived control, and weight loss: an application ofthe theory of planned behavior”, Journal of Personality and Social Psychology, Vol. 49 No. 4,pp. 843-51.

Serenko, K., Turel, O. and Yol, S. (2006), “Moderating roles of user demographics in the Americancustomer satisfaction model within the context of mobile services”, Journal of InformationTechnology Management, Vol. 17 No. 4, pp. 20-32.

Shashaani, L. (1997), “Gender differences in computer attitudes and use among college students”,Journal of Educational Computing Research, Vol. 16 No. 1, pp. 37-51.

Shih, H. (2004), “An empirical study on predicting user acceptance of e-shopping on the web”,Information & Management, Vol. 41 No. 3, pp. 351-68.

Age, gender andincome

131

Shin, D.-H. (2009), “Towards an understanding of the consumer acceptance of mobile wallet”,Computers in Human Behavior, Vol. 25 No. 6, pp. 1343-54.

Silva, E.B. (2000), “The cook, the cooker and the gendering of the kitchen”, The SociologicalReview, Vol. 48 No. 4, pp. 612-29.

Smith, S. and Comstock, D. (1995), “Teaching internet use to adult learners: the LANLexperience”, Proceedings for the 1995 Conference of the Council for the Higher EducationComputing Services (ERIC Document Reproduction Series ED 398 916), Roswell, NewMexico, November 8-10.

Soh, P., Yap, S. and Raman, S. (1992), “Impact of consultants on computerisation success in smallbusiness”, Information & Management, Vol. 22 No. 5, pp. 309-13.

Spreng, R.A., Mackenzie, S.B. and Olshavsky, R.W. (1996), “A re-examination of thedeterminants of consumer research”, Journal of Marketing, Vol. 60 No. 3, pp. 15-32.

Steenkamp, J.P. and Van Trijp, H.C.M. (1991), “The use of LISREL in validating marketingconstructs”, International Journal of Research in Marketing, 8 November, pp. 283-99.

Sun, H. and Zhang, P. (2006), “The role of moderating factors in user technology acceptance”,International Journal of Human-computer Studies, Vol. 64 No. 2, pp. 53-78.

Taylor, S. and Todd, P.A. (1995), “Assessing IT usage: the role of prior experience”, MIS Quarterly,Vol. 19 No. 4, pp. 561-70.

Trocchia, P.J. and Janda, S. (2000), “A phenomenological investigation of internet usage amongolder individuals”, Journal of Consumer Marketing, Vol. 17 No. 7, pp. 605-16.

Tsai, M.T. and Su, W. (2007), “The impact of cognitive fit and consensus on acceptance ofcollaborative information systems”, The Business Review, Vol. 8 No. 2, pp. 184-91.

Van den Poel, D. and Buckinx, W. (2005), “Predicting online-purchasing behaviour”, EuropeanJournal of Operational Research, Vol. 166 No. 2, pp. 557-75.

Venkatesh, V. and Morris, M.G. (2000), “Why don’t men ever stop to ask for directions?. Gender,social influence, and their role in technology acceptance and usage behaviour”, MISQuarterly, Vol. 24 No. 1, pp. 115-39.

Venkatesh, V., Morris, M. and Ackerman, P.L. (2000), “A longitudinal field investigation ofgender differences in individual technology adoption decision making processes”,Organizational Behavior and Human Decision Processes, Vol. 83 No. 1, pp. 33-60.

Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of informationtechnology: toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-78.

Vijayasarathy, L.R. (2004), “Predicting consumer intentions to use online shopping: the case foran augmented technology acceptance model”, Information & Management, Vol. 41 No. 6,pp. 747-62.

Wong, S.L. and Hanafi, A. (2007), “Gender differences in attitudes towards informationtechnology among Malaysian student teachers: a case study at University PutraMalaysia”, Educational Technology & Society, Vol. 10 No. 2, pp. 158-69.

Woodroof, J. and Burg, W. (2003), “Satisfaction/dissatisfaction: are users predisposed?”,Information & Management, Vol. 40 No. 4, pp. 317-24.

Wu, J.W., Chen, Y.C. and Lin, L.M. (2007), “Empirical evaluation of the revised end usercomputing acceptance model”, Computers in Human Behavior, Vol. 23 No. 1, pp. 162-74.

Yi, M.Y., Jackson, J.D., Park, J.S. and Probst, J. (2006), “Understanding information technologyacceptance by individual professionals: toward an integrative view”, Information &Management, Vol. 43 No. 3, pp. 350-63.

OIR35,1

132

Yu, J., Ha, I., Choi, M. and Rho, J. (2005), “Extending the TAM for a t-commerce”, Information &Management, Vol. 42 No. 77, pp. 965-76.

Zhang, Y. (2005), “Age, gender and internet attitudes among employees in the business world”,Computers in Human Behavior, Vol. 21 No. 1, pp. 1-10.

Zhang, J. (2009), “Exploring drivers in the adoption of mobile commerce in China”, The Journal ofthe American Academy of Business, Vol. 15 No. 1, pp. 64-9.

About the authorsBlanca Hernandez has a PhD in Business Administration and is a Lecturer in the Department ofMarketing and Business Studies at the University of Zaragoza (Spain). Her research interestsinclude the acceptance of new technologies, and e-commerce. Her work has been published injournals such as Online Information Review, Industrial Marketing Management, Technovation,Internet Research, International Journal of Information & Management, Interacting withComputers, Journal of Business Research, and the Journal of Business & Industrial Marketing,among others. Blanca Hernandez is the corresponding author and can be contacted at:bhernand@unizar.es

Julio Jimenez has a PhD in Business Administration and is a Professor in the Department ofMarketing and Business Studies at the University of Zaragoza (Spain). His research in adoptionand diffusion of innovations has been published in several journals, such as Research Policy,Industrial Marketing Management, International Journal of Information & Management,Internet Research, Online Information Review, Interacting with Computers and Technovation(five papers).

M. Jose Martın De Hoyos has a PhD in Business Administration and is a Senior Lecturer inthe Department of Marketing and Business Studies at the University of Zaragoza (Spain). Hermain research interests are online consumer behaviour and e-commerce. Her work has beenpublished in several journals, such as Industrial Marketing Management, Technovation, Journalof Business Research, International Journal of Information & Management, Internet Research,Interacting with Computers and Online Information Review.

Age, gender andincome

133

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.