inﬂuence of online shopping

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Influence of online shoppinginformation dependency

and innovativeness on internetshopping adoption

Enrique Bigne-Alcaniz, Carla Ruiz-Mafe, Joaquın Aldas-Manzano and Silvia Sanz-Blas

Department of Marketing, University of Vale ncia, Vale ncia, Spain

Abstract

Purpose – The paper’s purpose is to analyse the influence of online shopping informationdependency and innovativeness on the acceptance of internet shopping.

Design/methodology/approach – The impact of online shopping information dependency,domain-specific innovativeness and technology acceptance model (TAM) variables on futureshopping intention has been tested through structural equation modelling techniques. The sampleconsisted of 465 Spanish consumers who had never purchased online.

Findings – Data analysis shows that consumer innovativeness and online shopping informationdependency have a direct and positive influence on future online shopping intention and that the basicTAM hypotheses are fulfilled. Online shopping information dependency can be increased withinterfaces that are easier to use, but only if perceived usefulness remains high. Consumerinnovativeness positively influences internet exposure and the ease-of-use perception of the shoppingmedium, referred to throughout this paper as “shopping channel”.

Practical implications – This research enables companies to know which aspects of their

communication strategies to highlight in order to get non-purchasing web users to participate ine-shopping. Perceived ease of use and online shopping information dependency has a significantinfluence on shoppers’ willingness to purchase online. This shows that web content and design are keytools in the increase of future online purchasing. It is also recommended that managers target some of their advertising campaigns to the more innovative users.

Originality/value – There are still too few studies that analyse the effects of innovativeness andonline shopping information dependency on non-purchasing web users’ behaviour. This work aims tocombine the influence of online shopping information dependency, innovativeness and the traditionalTAM in order to construct an improved model for internet shopping acceptance. It will use anintegrated model to do so.

Keywords Innovation, Online operations, Shopping, Consumer behaviour, Electronic commerce, Spain

Paper type Research paper

IntroductionE-commerce adoption depends on the profile of potential consumers as not allconsumers accept an innovation at the same time (Rogers, 1962). The literature reviewshows that, among other factors, the relationship with the internet and the degree of

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

www.emeraldinsight.com/1468-4527.htm

Joaquın Aldas-Manzano acknowledges the financial support of the research project of theSpanish Ministry of Education and Science – FEDER (SEJ2005-02776).

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Refereed article received25 September 2007Approved for publication21 April 2008

Online Information Review

Vol. 32 No. 5, 2008

pp. 648-667

q Emerald Group Publishing Limited

1468-4527

DOI 10.1108/14684520810914025



receptiveness to innovation are factors which determine how quickly an internet userbecomes an online shopper (Citrin et al., 2000; Vrechopoulos et al., 2001).

In Spain, as in other countries, growing internet use has not been accompanied by asimilar growth in the number of online shoppers. Despite the fact that 40.8 per cent of

internet users based their purchase decisions in brick-and-mortar establishments oninformation they had obtained from the internet, only 13.6 per cent of the Spanishpopulation and 27.3 per cent of internet users shopped online in 2006 (Red.esObservatorio, 2007). Information on the users of the system is therefore important, as inthe final instance they are the ones who will guarantee the success or failure of this newshopping channel.

Individual attitudes towards new shopping channel adoption are based on classicalconsumer behaviour models (Ajzen and Fishbein, 1980; Howard and Sheth, 1969;Nicosia, 1966). The innovation dissemination process (Gatignon and Robertson, 1985)and information systems acceptance (Davis, 1989; Davis et al., 1989) have been used asthe explanatory framework for consumer behaviour analysis in relation to manydifferent products and markets.

In the last 20 years, different lines of research have focused on identifying certainfactors influencing the acceptance of information systems and have provided modelsand theoretical proposals. In particular, the technology acceptance model (TAM)introduced by Davis (Davis, 1989; Davis et al., 1989) has received considerableattention from the scientific community (Ahn et al., 2004; Deng et al., 2005; Lee et al.,2003; McKechnie et al., 2006; Sanchez-Franco and Roldan, 2005; Venkatesh and Davis,2000) and has been used to study any type of technological innovation. This modelexplains attitudes towards information systems and predicts use intentions andadoption and is the most widely used theoretical system in this field.

However, although the TAM has provided understanding of information systemsacceptance, more in-depth understanding is needed of the factors that contribute to the

acceptance of the internet as a shopping channel. More in-depth studies are still neededon the influence of attitudes towards innovation on the non-purchasing web user’sbehaviour (Vrechopoulos et al., 2001). It is also crucial to understand information usepatterns in order to develop effective strategies for attracting non-purchasing webusers (Klein, 1998; Shim et al., 2001). In addition, the group of internet users interestedin future online shopping can act as opinion leaders for other consumers (Modahl, 2000;Vrechopoulos et al., 2001).

In view of the above, this work aims to combine in an integrated model (ConsumerPersonal Characteristics Extended TAM (CPCETAM)) the influence of innovativeness,online shopping information dependency and the traditional TAM in order to constructan improved model for internet shopping acceptance. The study is divided into threeparts. In the first section the conceptual model is presented focusing on the rationale for

the constructs used to expand the TAM and deriving testable hypotheses. In thesecond or methodology section, design, sampling and measures are described andvalidated. In the third, the results, based on a sample of 465 Spanish internet users, arepresented and managerial implications are discussed.

Theoretical frameworkPurchase intention refers to a mental state that reflects the consumer’s decision toacquire a product or service in the immediate future (Howard, 1989). In the context of

Online shoppinginformation

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virtual shopping, this would be the decision to use the internet as a new shoppingchannel. The proposed model for integrating the influence of innovativeness, onlineinformation use patterns and attitudinal TAM variables on internet shopping intentionis presented below.

Technology acceptance model The TAM was developed by Davis (1989) and by Davis et al. (1989) to explain theacceptance of information technology for different tasks and may be used to predictinternet shopping intention (McKechnie et al., 2006; O’Cass and Fenech, 2003). Thismodel establishes that the intention to use a technology is determined by theindividual’s attitude towards using that technology. That attitude is, in turn,determined by the technology’s perceived usefulness and perceived ease of use.

Davis et al. (1989) identified perceived usefulness and perceived ease of use as thebasic determining factors in information system acceptance. These authors definedperceived usefulness as the degree to which a consumer believes that the use of asystem will increase his or her performance. Specifically, it refers to effectiveness at

work, productivity (understood as time savings) and the relative importance of thesystem for the individual’s work. Perceived ease of use refers to the degree to which aconsumer believes that no effort will be required to use the system, with effort beingunderstood to include both physical and mental effort, and how easy it is to learn to usethe system (Davis et al., 1989).

Both the perceived usefulness and the perceived ease of use influence anindividual’s attitude towards a technology. Attitude and perceived usefulness in turnpredict the individual’s behaviour intention. In addition, perceived ease of useinfluences perceived usefulness.

We therefore posit the following hypotheses in relation to the TAM:

H1. Perceived ease of use of the internet as a shopping channel has a positive

influence on the perceived usefulness of the internet as a shopping channel.

H2. Perceived ease of use of the internet as a shopping channel has a positiveinfluence on attitudes towards the internet as a shopping channel.

H3. Perceived usefulness of the internet as a shopping channel has a positiveinfluence on attitudes towards the internet as a shopping channel.

H4. Perceived usefulness of the internet as a shopping channel has a positiveinfluence on future online shopping intention.

H5. The attitude towards the internet as a shopping channel has a positiveinfluence on future online shopping intention.

Innovativeness and online shopping A literature review of new product adoption revealed several works that proposemethods for distinguishing between categories of adopters (Bass, 1969; Rogers, 1962) andthat try to characterise the behaviour of the individuals in the different categories (Brown,1982; Donthu and Garcia, 1999; Eastlick and Lotz, 1999; Vrechopoulos et al., 2001).

Innovativeness is a concept linked to the new product adoption process that hasreceived considerable attention from researchers (Hirschman, 1980; Midgley andDowling, 1978; Robertson, 1971). This construct of the personality of individuals

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reflects their degree of adoption of products and ideas that are new in their individualexperience. Rogers and Shoemaker (1971) considered that innovativeness is the degreeto which an individual adopts an innovation before other members of his or her socialsystem. Midgley and Dowling (1978) defined innate innovativeness as the degree to

which an individual is receptive to new ideas and makes innovative decisionsindependently of the experiences related by other individuals, and maintained that firstadopters are those with the greatest innate innovativeness. In contrast, Hirschman(1980) considered that innovativeness is influenced more by the social system than bythe individual’s personality, and that an individual’s desire to seek new stimuli is asegmentation variable which provides three groups of consumers with differentdegrees of innovativeness – adopters (individuals who adopt a product), vicariousconsumers (individuals who seek information on new products and services) and users(individuals who apply new uses to existing products).

Although many researchers have used different techniques to measureinnovativeness, two main approaches to the concept can be distinguished – general

innovativeness and innovativeness applied to a specific domain. Joseph and Vyas(1984) focused on a cognitive perspective, considering that innovativeness incorporatesthe individual’s intellectual, perceptual and attitudinal characteristics. Generalinnovativeness reflects openness and an individual’s search for new experiences andit is a significant predictor of shopping intention (Craig and Ginter, 1975; Joseph andVyas, 1984).

A limitation of the previous definition is its degree of abstraction and its generalistcharacter as innovativeness can be associated with a specific product or service ratherthan with a generic characteristic of an individual’s personality. Owing to this

limitation, Goldsmith and Hofacker (1991) developed a measurement scale forinnovativeness in a specific domain. Domain specific innovativeness is the individual’stendency to try innovations in products, services or processes in his or her area of interest (Goldsmith and Hofacker, 1991). Domain specific measures are more predictiveof the purchase of new items than global innovativeness (Goldsmith et al., 1995;Goldsmith and Hofacker, 1991). Later research (Blake et al., 2003; Citrin et al., 2000;Goldsmith, 2000, 2001) has applied the domain specific innovativeness scale to onlineshopping and has shown a direct and positive influence of this variable both inthe search for pre-online purchase information and the decision to purchase throughthe internet.

A set of studies relates consumer innovativeness and intention to shop. Eastlick andLotz (1999) showed that innovators are heavy users of interactive electronic-shoppingmedia and that the strongest predictors of potential innovator group membership werethe perceived advantage of interactive shopping innovation over traditional shopping

channels and its compatibility with lifestyles. The study by Limayem et al. (2000)found that innovativeness influences internet shopping behaviour both directly andindirectly through consumers’ attitudes and intentions. Goldsmith (2000) also foundevidence that the frequency of online buying and intent to buy online in the future werepredicted by general innovativeness, an innovative predisposition towards buyingonline and involvement with the internet. Citrin et al. (2000) supported this conclusion

with their findings that domain-specific innovativeness along with internet usagedirectly influences consumers’ adoption behaviour of internet shopping.


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To complement the contributions of the above studies, we propose the followinghypothesis:

H6. Innovativeness towards online shopping has a favourable influence on the

future online shopping intention.Indirect effects of innovativeness towards future online shopping intention, mediated byTAM variables, can also be expected. According to innovation diffusion theory, earlieradopters, given their knowledge, experience, technical competence and high aspiration(Moore, 1991; Rogers, 1962), should consider the same technology to be easier to use and

less challenging than later adopters. Therefore, highly innovative individuals mayperceive online shopping as easier to use than less innovative consumers.

Previous research has found that individuals’ innovativeness enhances perceivedease of use. Research by Yi et al. (2006) across two innovations (online buying and PDA)showed that individual innovativeness is one significant antecedent, among others, of ease of use. Lewis et al. (2003) also found that innovativeness had a significant positive

effect on ease of use in the context of the adoption and use of internet technologies byfaculty and instructors in their teaching activities. Agarwal and Karahanna (2000)demonstrated that personal innovativeness indirectly influences behavioural intentionvia its effect on cognitive absorption, which is in turn a significant determinant of ease of

use. Jashapara and Tai (2006) showed that personal innovativeness with informationtechnology influences positively the perceived ease of use of a virtual learningenvironment. Therefore, we hypothesise that:

H7. Innovativeness towards online shopping has a favourable influence on

perceived ease of use of the internet as a shopping channel.

Hirschman (1980) clearly stated that novelty seeking is an inherent characteristic of

innovators and that novelty seeking would seem to represent an innate search forinformation. She argued that a possible explanation for this linkage is that theconsumer who has sought and stored more information is likely to be better equipped

for novel problem circumstances and can improve his or her performance to adopt newproducts.

The acquisition of novel information may be achieved through many differentsources. When Hirschman (1980, p. 285) wrote her paper in 1980 the internet was not a

feasible medium from which to acquire information, but we believe that the reasons sheprovided to explain why innovators would be more likely to read magazines can beapplied directly to the internet:

[. . .] every issue [web page] contains novel information and a magazine subscription

[exposure time to internet] represents a commitment by the consumer to acquire novel data.An information-rich medium, such as a magazine [the internet], allows the individual to

absorb the accumulated experiences of others in an accessible and low-risk form.

Recent research has provided evidence that Hirschman’s ideas are applicable to theinternet. Rangaswamy and Gupta (1999) reported than innovative buyers spent more

time per week on the internet than did other groups of buyers. Goldsmith (2001)showed that internet innovativeness is positively correlated with use of the internet.Thus, we are able to hypothesise that:

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H8. Innovativeness towards online shopping has a favourable influence onindividual internet exposure levels.

Internet users modify their surfing and shopping behaviour as they gain experience in

new environments (Dahlen, 2002). Thus, expert users surf more quickly, have shortersessions and visit a smaller number of web sites. In addition, as their exposure andknowledge of the medium increases, they enjoy their surfing experience more(Csikszentmihalyi, 1997; Ellis et al., 1994; Novak et al., 2000) and develop more positiveattitudes towards using the system for shopping (Park and Kim, 2003; Yoon et al., 2002).

Although previous studies relate internet exposure to purchase decision, the TAMpostulates that the impact of external variables is mediated by perceived usefulnessand perceived ease of use.

In view of the above, we posit the following research hypothesis:

H9. Individual internet exposure levels have a positive influence on perceived easeof use of the internet as a shopping channel.

Online shopping information dependency and internet shopping intentionAccording to the individual medium dependency theory (Grant et al., 1991; Loges,1994) individuals achieve some of their personal and collective objectives by having toaccess information resources that are controlled by the mass media such as the internetand television. In this sense, individual media dependency is defined as “a relationwhere the individual’s capacity to reach his or her objectives, depends to a certainextent on the information resources in the medium” (Ball-Rokeach et al., 1984, p. 3).

Individual medium dependency has three dimensions or categories – understanding, orientation and play (Ball-Rokeach, 1985, 1989; Ball-Rokeach et al.,1984; Defleur and Ball-Rokeach, 1989; Grant, 1996; Grant et al., 1991; Loges, 1994;Loges and Ball-Rokeach, 1993; Skumanich and Kintsfather, 1998). Understanding

focuses on the individual’s need to have a basic understanding of themselves and tofind sense in the world that surrounds them. Orientation refers to the need to obtain aguide in order to behave correctly with other people. Play is also an important way tolearn social roles, norms and values, and it also provides escape mechanisms andrelease from tension (Grant et al., 1991).

Dependency on the medium’s information resources may cause cognitive, affectiveand behavioural changes in people who are regularly exposed to them (Ball-Rokeach,1989; Ball-Rokeach et al., 1984; Grant, 1996; Grant et al., 1991). For example, in terms of behavioural effects, the purchase of products and services may be intensified whenindividual medium dependency is high (Ball-Rokeach, 1985; Defleur and Ball-Rokeach,1989). Previous studies that focused on the television medium (Grant et al., 1991;Skumanich and Kintsfather, 1998) have shown that individual dependency on that

medium is a significant predictor of teleshopping behaviour. Other studies have founda direct, positive relationship between internet user dependency levels and present andfuture online purchase decisions (Patwardhan and Yang, 2003; Ruiz and Sanz, 2006).

Stigler (1961) found that consumers analyse the costs and benefits of theinformation search and abandon it when they perceive that the marginal costs (wasteof time, money, transport, etc.) are greater than the benefits (duration of the search,variety of information sources, etc.) that they obtain. Other studies have also shownthat the nature of the information sources may influence purchase behaviour,


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for example, where consumers have to search prepurchase information through asingle channel, as this reduces the effort involved (Engel et al., 1995). Klein (1998)developed an interactive behaviour model where the information search processes arepredictors of purchase behaviour, with the advantages of the internet being useful in

the case of search goods due to the low-perceived costs of obtaining information.Internet users perceive that the utility of the internet to support the prepurchase

information process is one of its most outstanding characteristics (Maignan and Lukas,1997; Rowley, 2000) as it is the most appropriate channel for comparing differentpurchase options (Dickson, 2000). The internet allows consumers to identify their mostuseful options easily owing to web tools such as portals and search engines that makeit possible to find relevant information for the purchase decision, reducing informationsearch costs (Haubl and Trifts, 2000; Hoffman and Novak, 1996; Widing and Talarzyk,1993). Several authors suggest that interactivity increases consumers’ skillsin exploring and analysing the available information (Ariely, 2000; Hoffman andNovak, 1996). It is to be expected, therefore, that consumers will increasingly usevirtual environments to consider different purchase options.

In addition, the increasing amount of online information which is personalised inaccordance with the consumer’s previous searches or purchases helps them to makebetter purchase decisions and consequently develop a more favourable attitude tosellers’ web sites. It is also worth noting that according to Petty and Cacioppo (1986),consumers’ attitudes are favourable when they process relevant information formaking their purchase decisions. As more information is available on the internet,consumers tend to make a greater effort to process it and therefore a positive change intheir attitude is to be expected.

Bearing in mind the results in the literature, we test a similar effect with thefollowing hypothesis:

H10. As online shopping information dependency increases, so does the future

online shopping intention.

But online information dependency is at the same time determined by several TAMvariables.

As we have stated, media system dependency theory sees individuals as havingpersonal goals. People will develop dependency relations with the media as a means of attaining those objectives (Grant et al., 1991), as we believe, only if the media allow theindividual to attain the goal, that is, if it is a useful instrument in that task. We agree withBall-Rokeach (1985, p. 495) when she states that media dependency will change “asperceptions of the utility of media resources change”. If consumers believe that theinternet as a shopping channel enables them to accomplish shopping tasks morequickly, to make better purchase decisions or to save money, the perception of the utility

of the medium will increase, as will internet dependency according to Ball-Rokeach’s(1985) hypothesis.

Therefore, we hypothesise that:

H11. As the perceived usefulness of the internet as a shopping channel increases, sodoes the online shopping information dependency of the consumer.

Greater perceived ease of use may activate internet dependency, since consumers canbecome aware of the internet as a tool that not only allows them to fulfil their

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objectives, but also with less effort than other channels. In other words, if consumerscan attain the same objective through two different distribution channels (of the sameusefulness) it seems rational to rely on the channel that is easier to use and to becomemore dependent on it with time.

Several studies report that the growing dependency on the internet to search forinformation is due to several benefits, one of which is that it is an easy-to-use way of accessing price and product information (Bei et al., 2004; Porter, 2001). Riffe et al. (2007)posit that easy use of the internet for detailed, in-depth information about specialisedtopics increases individuals’ internet dependency more than general and non-specificexposure does. This relation is sometimes so strong thatit may become counterproductive.For instance, MacDonald and Dunkelberger (1998) reported that the growing dependencyof undergraduate students on full-text databases due to their ease of use is biasing theirresearch and assignments as they exclude all other information sources. Independent of the positive or negative consequences of dependency, this study also expects to observe apositive relationship between perceived ease of use of the internet and dependency on thismedium:

H12. As the perceived ease of use of the internet as a shopping channel increases,so does the online shopping information dependency of the consumer.

Figure 1 shows the extended TAM examined here (CPCETAM). The model belowshows the influence of innovativeness, online shopping information dependency andTAM variables on future online shopping intention.

MethodSample and data collectionThe sample consisted of 465 internet users over the age of 18. The fieldwork wasdeveloped in Spain from April to May 2006 and the sample consisted of consumers

who had never purchased online.A questionnaire with closed-end questions was used for this study. Sampling was

by gender and age quotas based on internet user characteristics periodically examinedby the Spanish Association of Electronic Commerce Firms in its study of business-to-consumer e-commerce in Spain 2005 (AECEM, 2006), which is the mostimportant directory of internet users in Spain.

Table I displays demographic variables associated with the sample. Of the totalsample, 51.4 per cent were men and 48.6 per cent were women. A large percentage of theinterviewees belonged to the age segment between 25 and 49 (58 per cent), weremedium-educated (51.2 per cent) and had an above average level of income (42.1 per cent).

Measures

As illustrated in Table II, the constructs used in our study were adapted from previousstudies and were measured by multiple item five-point Likert-type scales, with theexception of internet exposure (one item) and future shopping intention (one item).

The scale items for perceived ease of use and perceived usefulness were adapted fromthe measurement defined by Davis (1989) and Ahn et al. (2004). Attitude to e-commercewas measured using the personal involvement inventory scale (Zaichkowsky, 1994)with modifications to suit the environment of internet shopping. Innovativeness wasmeasured using a four-item scale based on the domain specific scale developed by


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Figure 1.Consumer personalcharacteristics extendedTAM (CPCETAM)

Characteristics Percentage ( N ¼ 465)

Gender Male 51.4Female 48.6

EducationNo formal education 0.4Primary 11.2Secondary 51.2University 37.2

Age24 or under 27.425-34 28.035-49 30.050-64 12.0

65 þ 2.6 Income a (monthly)No income (family dependent) 20.2Below average 17.4Around average 20.4Above average 34.5Well above average 7.6

Note: aMonthly income average – e900Table I.Sample demographics

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C o n s t r u c t

S c a l e

t y p e

I t e m c o d i n g

I t e m d e s c r i p t i o n s

S o u r c e

A t t i t u d e t o o n l i n e s h o p p i n g

L i k e r t 5

A T T I 1

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s e n j o

y a b l e

A d a p t e d f r o m Z a i c h k o w s k y ( 1 9 9 4 ) ,

A T T I 2

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s c o n v

e n i e n t G o l d s m i t h ( 2 0 0 2 ) a n d O ’ C a s s

a n d F e n e c h

( 2 0 0 3 )

A T T I 3

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s a b s o r b i n g

A T T I 4

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s a t t r a c t i v e

A T T I 5

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s i n t e r e s t i n g

A T T I 6

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s w o r

t h i t

A T T I 7

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s p l e a

s a n t

A T T I 8

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s s e c u r e

A T T I 9

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s n e c e s s a r y

A T T I 1 0

U

s i n g t h e i n t e r n e t f o r s h o p p i n g i s a g o o d i d e a

P e r c e i v e d u s e f u l n e s s

L i k e r t 5

U S E F U L 1 U

s i n g t h e i n t e r n e t f o r s h o p p i n g e n a b l e s m e t o

a

c c o m p l i s h s h o p p i n g t a s k s m o r e q u i c k

l y

A d a p t e d f r o m D a v i s ( 1 9 8 9 ) a n d A h n e t a l .

( 2 0 0 4 )

U S E F U L 2 U

s i n g t h e i n t e r n e t f o r s h o p p i n g h e l p s m e t o

m

a k e b e t t e r p u r c h a s e d e c i s i o n s

U S E F U L 3 U

s i n g t h e i n t e r n e t f o r s h o p p i n g i m p r o v e s t h e

p

e r f o r m a n c e o f m y s h o p p i n g t a s k s

U S E F U L 4 U

s i n g t h e i n t e r n e t f o r s h o p p i n g s a v e s

m e

m

o n e y

U S E F U L 5 U

s i n g t h e i n t e r n e t f o r s h o p p i n g i m p r o v e s t h e

q

u a l i t y o f m y s h o p p i n g t a s k s

U S E F U L 6 U

s i n g t h e i n t e r n e t f o r s h o p p i n g i n c r e a s e s t h e

p

r o d u c t i v i t y o f m y s h o p p i n g t a s k s

P e r c e i v e d e a s e o f u s e

L i k e r t 5

E A S E 1

I

t h i n k t h a t I w o u l d fi n d i t e a s y t o l e a r n h o w

t o s h o p o n l i n e

A d a p t e d f r o m D a v i s ( 1 9 8 9 ) a n d A h n e t a l .

( 2 0 0 4 )

E A S E 2

I

t h i n k t h a t i t w o u l d b e p o s s i b l e f o r m

e t o

s

h o p o n l i n e w i t h o u t t h e h e l p o f a n e x p

e r t

E A S E 3

I

t h i n k t h a t I w o u l d h a v e n o p r o b l e m s

i n t e r a c t i n g w i t h t h e i n t e r n e t w h e n s h o

p p i n g

( c o n t i n u e d )

Table II.Measurement scales


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C o n s t r u c t

S c a l e

t y p e

I t e m c o d i n g

I t e m d e s c r i p t i o n s

S o u r c e

E A S E 4

I

t h i n k t h a t I c o u l d b e c o m e s k i l f u l a t o

n l i n e

s

h o p p i n g

E A S E 5

I

t h i n k t h a t s h o p p i n g o n l i n e d o e s n o t r e q u i r e

a

l o t o f m e n t a l e f f o r t

E A S E 6

I

t h i n k t h a t i t i s e a s y t o u s e t h e i n t e r n e t

t o fi n d

p

r o d u c t s t h a t I w a n t t o b u y

I n n o v a t i v e n e s s

L i k e r t 5

I N N 1

I

t h i n k I w o u l d b e t h e fi r s t i n m y c i r c l e o f

f r i e n d s t o k n o w t h e s i t e s w h e r e I c a n s h o p

o

n l i n e

A d a p t e d f r o m G o l d s m i t h a n d

H o f a c k e r

( 1 9 9 1 )

I N N 2

I

t h i n k I w o u l d b e t h e fi r s t i n m y c i r c l e o f

f r i e n d s t o s h o p o n l i n e

I N N 3

I

t h i n k I k n o w m o r e a b o u t s h o p p i n g o

n l i n e

t h a n m y c i r c l e o f f r i e n d s

I N N 4

I

t h i n k I w o u l d s h o p o n l i n e e v e n i f I d i d n o t

k

n o w a n y o n e w h o h a d d o n e i t b e f o r e

O n l i n e s h o p p i n g i n f o r m a

t i o n

d e p e n d e n c y

L i k e r t 5

D E P 1

I N T E R N E T h e l p s y o u t o d e c i d e w h e r e

t o b u y

c

e r t a i n p r o d u c t s o r s e r v i c e s

A d a p t e d f r o m G r a n t ( 1 9 9 6 )

D E P 2

I N T E R N E T h e l p s y o u t o d e c i d e w h a t

t o b u y

D E P 3

I N T E R N E T h e l p s y o u t o p l a n w e e k e n d

t r i p s / e x c u r s i o n s

I n t e r n e t e x p o s u r e

1 i t e m

E X P

H

o w o f t e n d o y o u a c c e s s t h e i n t e r n e t ?

6

¼

S e v e r a l t i m e s a d a y ; 5 ¼

o n c e a

d a y ;

4

¼

s e v e r a l t i m e s a w e e k ; 3 ¼

o n c e a

w e e k ;

2

¼

o n c e e v e r y 1 5 d a y s ; 1 ¼

l e s s t h a

n o n c e

a

m o n t h

G o l d s m i t h ( 2 0 0 2 ) a n d R u i z a n d S a n z ( 2 0 0 6 )

F u t u r e s h o p p i n g i n t e n t i o n

1 i t e m

I N T

W

o u l d y o u b u y a p r o d u c t / s e r v i c e o n l i n e i n

t h e n e x t y e a r ? 5 ¼

Y e s , d e fi n i t e l y ;

4

¼

p r o b a b l y y e s ; 3 ¼

i n d i f f e r e n t ;

2

¼

p r o b a b l y n o t ; 1 ¼

n o , d e fi n i t e l y n o t

G o l d s m i t h ( 2 0 0 2 )

Table II.

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Goldsmith and Hofacker (1991). Online shopping information dependency was derivedfrom the individual action orientation dimension of the scale provided by Grant (1996).The two items used to measure internet exposure and future purchase intention weretaken from Ruiz and Sanz (2006) and Goldsmith (2002). Table II describes how the

variables used in this research were measured.

Validation of the measurement model A confirmatory factor analysis was developed to validate the measurement model(Table AI in the Appendix). To guarantee convergent validity, items with factorloadings which were not significant or below 0.6 (Bagozzi and Baumgartner, 1994;Bagozzi and Yi, 1988) and those for which the Lagrange multiplier test suggestedsignificant relations over a different factor to the one they were indicators for (Hatcher,1994) were eliminated. The final measurement model is also reliable as all Cronbach’s(1951) a s are above the recommended value of 0.7 (Churchill, 1979) and compositereliability indexes are also higher than 0.7 (Fornell and Larcker, 1981). No evidence of alack of discriminant validity is found, either applying the confidence interval criterion(Anderson and Gerbing, 1988) or the average variance extracted criterion (Fornell andLarcker, 1981), as can be seen in Table AII in the Appendix. Nomological validity isassured as the difference between the measurement model and the theoretical model(structural model) x 2s is not significant (Anderson and Gerbing, 1988; Hatcher, 1994).

ResultsAfter evaluating the psychometric properties of the measurement instrument, thestructural model shown in Figure 1, which synthesises the hypotheses posited, wasestimated. Hypotheses were tested using structural equation models. Steenkamp andBaumgartner (2000) highlighted two main advantages of this technique. First,structural equation models allow measurement error to be explicitly incorporated into

models and its influence on the degree of fit to be analysed. Second, unlike multipleregressions, relations between model variables can be studied simultaneously asseveral dependent variables can be considered in the same model and the samevariable can be at the same time an endogenous and exogenous variable regarding theother variables in the model.

Raw data screening showed evidence of non-normal distribution (Mardia’scoefficient normalised estimate ¼ 16.5). Although other estimation methods havebeen developed for use when the normality assumption does not hold, therecommendation of Chou et al. (1991) and Hu et al. (1992) of correcting the statisticsrather than using a different estimation model has been followed. So, robust statistics(Satorra and Bentler, 1988) will be provided.

The empirical estimates for the main-effects model are shown in Table III.

The results indicate that the data fit our conceptual model acceptably (S-Bx 2 ¼ 295.03,df ¼ 124, p ¼ 0.00; RMSEA ¼ 0.055; NFI ¼ 0.90; NNFI ¼ 0.92; CFI ¼ 0.94).Modification indices do not provide any indication of misfit of the structural model,suggesting that there is no need to include any new path between constructs in themodel.

The results obtained show that the perceived ease of use of the internet as ashopping channel has a significant positive influence on the set of variables which actas mediators in future shopping intention – the perceived usefulness of the internet as


659



a shopping channel ( l¼ 0.613;

p, 0.01;

H1 ), attitude towards online shopping

( l ¼ 0.133; p , 0.05; H2 ) and dependency on the internet to obtain information for thepurchase process ( l ¼ 0.237; p , 0.0; H12 ). The results also show how the perceivedease of use of the technology is highly conditioned by internet user experiencemeasured by exposure to the medium ( l ¼ 0.216; p , 0.01; H9 ), that is, users whoaccess the internet more frequently perceive less difficulty associated with its use.Consequently, while perceived ease of use does not directly influence purchaseintention, it does activate the other variables that directly influence the intention tobecome an electronic shopper.

As Table III shows, perceived usefulness of online shopping influences futureshopping intention both directly ( l ¼ 0.188; p , 0.05; H4 ) and through its influence ononline shopping attitude ( l ¼ 0.569; p , 0.01; H3 ), which also has a positive effect on

online shopping intention ( l ¼ 0.213; p , 0.01; H5 ). These results confirm that TAM isa valid model to explain online shopping intention. The question is whether consumerpersonal characteristics can significantly improve it or not.

Focusing on one of these personal characteristics of the consumers, dependency onthe internet to obtain information in the shopping process has a positive andsignificant impact on future online shopping intention ( l ¼ 0.192; p , 0.01; H10 ). Asexpected, dependency is heavily conditioned by how useful consumers perceive theinternet to be as a shopping channel ( l ¼ 0.642; p , 0.01; H11 ).

Model block HypothesesStandardised

loadingRobustt -value

Original TAM H1. Perceived ease of use! Perceived

usefulness 0.613 * * 10.15 H2. Perceived ease of use! Attitude to online

shopping 0.133 * * 1.99 H3. Perceived usefulness! Attitude to online

shopping 0.569 * * 7.52 H4. Perceived usefulness! Online shopping

intention 0.188 * * 2.32 H5. Attitude to online shopping ! Online

shopping intention 0.213 * * 3.52Innovativeness H6. Innovativeness! Online shopping intention 0.304 * * 6.40role H7. Innovativeness! Perceived ease of use 0.284 * * 4.08

H8. Innovativeness! Internet exposure 0.248 * * 4.53 H9. Internet exposure! Perceived ease of use 0.216 * * 3.62

Onlineinformation H10. Online shopping informationdependency! Online shopping intention 0.192 * * 2.75dependency role H11. Perceived usefulness! Online shopping

information dependency 0.642 * * 6.67 H12. Perceived ease of use! Online shopping

information dependency 0.237 * 2.37S-B x

2 (124 df ) ¼ 295.03 ( p , 0.01); NFI ¼ 0.90; NNFI ¼ 0.92; CFI ¼ 0.94; IFI ¼ 0.94;RMSEA ¼ 0.055

Notes: * p , 0.05; * * p , 0.01Table III.Hypotheses testing

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The role of the second personal characteristic of the consumers, their degree of innovativeness, also reveals as crucial to improve the explanatory power of TAM.Innovativeness not only directly and positively influences consumers’ future intention toacquire products or services using the internet ( l ¼ 0.304; p , 0.01; H6 ), but it also

influences the perceived ease of use of the channel both directly( l ¼ 0.284; p , 0.01; H7 )and by making them spend more time connected to the web ( l ¼ 0.248; p , 0.01; H8 ).

Discussion and conclusionsThe main contribution of this research lies in proposing and empirically verifying amodel that integrates the influence of innovativeness, online shopping informationdependency and the traditional TAM on future online shopping intention.

The indirect influence of perceived ease of use on future shopping intention throughattitudes highlights the central role of the perceived ease of use of the internet forshopping in the adoption of this shopping channel. This result has importantmanagerial implications. If an e-commerce company wishes to increase the number of

customers, in addition to service level and quality considerations, it must also takegreat care to design a user-friendly web site and include elements which facilitatepurchase.

Furthermore, this study emphasises the importance of general internet use in thepopulation as the preceding step to growth in electronic commerce (Yoon et al., 2002;Park and Kim, 2003), given the central role of user experience (exposure) as anantecedent to perceived ease of use.

The direct and positive influence of innovativeness towards online shopping onfuture internet shopping intention confirms similar results obtained in prior studieswhich showed that a positive attitude to electronic channels is a significant predictor of adoption (Chau and Lung, 1998; Eastlick and Lotz, 1999; Goldsmith, 2000; Limayemet al., 2000; O’Cass and Fenech, 2003). This result also highlights that marketing

managers need to be able to do more than just identify innovators. They should targetsome of their advertising campaigns towards the more innovative users. As Moore(1991) suggests, innovators provide companies with great feedback early in the designcycle and may become a supporter who will influence buyers. If the bulk of non-shopper internet users need word-of-mouth promotion before they will adoptonline shopping, innovators can initiate this dialogue.

Targeting innovators has an additional attraction for managers. We know thatthese individuals are usually early adopters of many products and therefore they arewell able to bear price-skimming strategies, that is, charging a relatively high price fora short time where a new, innovative or much-improved product is launched onto amarket to “skim off” customers who are willing to pay more to have the productsooner. Prices are lowered later when demand from the early adopters falls. This

strategy is probably the most profitable in terms of margin for the manufacturer.Finally, we have shown the direct, positive influence of prepurchase online

information dependency on future online shopping intention. This result is consistentwith earlier studies in different cultural contexts to ours which showed that internetusers who use online information to support their purchase decisions develop a greateronline shopping intention (Citrin et al., 2003; Shim et al., 2001), and that onlinedependency has a direct positive influence on online shopping intention (Patwardhanand Yang, 2003; Ruiz and Sanz, 2006). But it should notbe forgotten that this dependency


661



is highly conditioned by how useful and easy to use consumers perceive the internetchannel to be. That means that dependency should be carefully built by adding value tothe medium. Overloading of unstructured information, privacy concerns or poorlydesigned storefronts, among others, can reduce internet usefulness and ease of use as a

shopping channel and subsequently online information dependency.Therefore, we can conclude that consumers develop complex shopping strategies in

which achievement of the final objective (purchase intention) is preceded by securing aset of prior objectives (prepurchase information). This result may also have importantimplications in terms of e-commerce web site design. While information is alwaysimportant for decision making, in e-commerce it appears to be even more so.Differentiation in the amount and quality of information on the product or servicebeing offered thus becomes a significant competitive instrument.

These conclusions have some limitations and open new lines for future research.A possible limitation is that the study has focused on measuring attitudes (futureshopping intention), which do not always become behaviours. Thus, possible futureresearch could contrast the proposed model with a sample of internet shoppers to see if

the results obtained remain valid.Another limitation is that there has been no consideration of the influence of the

characteristics of goods and services on shopping behaviour. Prior studies show thatthe greater the perceived risk for consumers, the greater the prepurchase informationsearch effort (Dowling, 1986; Mitchell and Boustani, 1994). We therefore propose as afuture line of research to apply the model to the purchase of search and credence goods.Given that the perceived risk in the internet is greater than in traditional environments,another future line of research would be to analyse the influence of perceived purchaserisk on the different variables analysed.

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Appendix

Corresponding authorCarla Ruiz-Mafe can be contacted at: [email protected]

1 2 3 4 5

1. Perceived usefulness 0.75 0.60 * * 0.20 * * 0.65 * * 0.62 * *

2. Perceived ease of use [0.51;0.69] 0.66 0.33 * * 0.47 * * 0.55 * *3. Innovativeness [0.09;0.32] [0.19;0.46] 0.82 0.17 * * 0.084. Attitude to online shopping [0.59;0.71] [0.36;0.58] [0.05;0.29] 0.77 0.44 * *

5. Online information dependency [0.53;0.72] [0.43;0.67] [-0.04;0.21] [0.33;0.55] 0.77

Notes: * p , 0.05; * * p , 0.01. Diagonal represents the square root of the average variance extracted;while above the diagonal the shared variance (squared correlations) are represented. Below thediagonal the 95 per cent confidence interval for the estimated factors correlations is provided

Table AII.Validation of the final

measurement model –discriminant validity

Variable Indicator Factor loading Robust t -value CA CR AVE

Perceived usefulness USEFUL2 0.740 * * 19.03 0.87 0.87 0.57USEFUL3 0.780 * * 20.86USEFUL4 0.760 * * 19.59USEFUL5 0.732 * * 18.01USEFUL6 0.758 * * 19.51

Perceived ease of use EASE1 0.750 * * 16.99 0.74 0.75 0.43EASE3 0.600 * * 12.04EASE4 0.646 * * 14.01EASE6 0.629 * * 12.88

Innovativeness INN1 0.701 * * 8.81 0.78 0.80 0.67INN2 0.920 * * 10.67

Attitude to online shopping ATTI4 0.743 * * 16.82 0.81 0.81 0.59ATTI5 0.873 * * 20.29

ATTI7 0.678 * * 14.03Information dependency DEP1 0.840 * * 18.27 0.73 0.74 0.59

DEP3 0.685 * * 13.24S-B x 2 (94 df) ¼ 252.31 ( p , 0.01); NFI ¼ 0.90; NNFI ¼ 0.92; CFI ¼ 0.94; IFI ¼ 0.94;RMSEA ¼ 0.06

Notes: * p , 0.05; * * p , 0.01. CA ¼ Cronbach’s a; CR ¼ composite reliability; AVE¼ averagevariance extracted

Table AI.Validation of the final

measurement model –reliability and convergent

validity


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