The impact of self-efficacy, ease of use and usefulness on e-purchasing: An analysis of experienced e-shoppers

Interacting with Computers 21 (2009) 146–156

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The impact of self-efficacy, ease of use and usefulness on e-purchasing: An analysis of experienced e-shoppers Blanca Hernandez *, Julio Jimenez, M. Jose Martin Faculty of Economics and Business Studies, Economics and Business Administration Department, University of Zaragoza, C/ Gran Vía 2, C.P. 50005, Zaragoza, Spain

a r t i c l e

i n f o

Article history: Received 4 June 2008 Received in revised form 4 November 2008 Accepted 14 November 2008 Available online 24 November 2008 Keywords: e-Commerce Experienced e-shopper Future repurchasing behaviour Present e-purchasing behaviour Perceived self-efficacy

a b s t r a c t The objective of the present research is to study the Internet purchasing behaviour of consumers who are experienced with the channel, employing a dual perspective for the analysis: (1) present e-purchasing behaviour and (2) future repurchasing behaviour measured through repurchasing intentions. On the basis of this approach, we attempt to understand the effect of perceived self-efficacy, ease of use and usefulness on both types of behaviour and the links between them. Furthermore, the research includes other variables related to Internet experience, extracted from models widely tested in the literature. These variables, namely, acceptance, frequency of use and satisfaction with the Internet, act as antecedents of e-purchasing behaviour and permit a deeper analysis of the consumer. The results obtained show that self-efficacy and usefulness are important perceptions in explaining the behaviour of experienced consumers, while ease of use does not have a significant influence. Ó 2008 Elsevier B.V. All rights reserved.

1. Introduction The objective of most research in e-commerce has been to analyse the process of the adoption of the new channel and compare it with the offline market, that is, to understand the factors which lead to the first e-purchase being made (some examples of this research are Chen et al., 2002; Klopping and McKinney, 2004). Nevertheless, in recent years, e-commerce has grown greatly and there are more and more shoppers who have carried out several purchases. This, together with the fact that e-purchasing experience modifies the behaviour of individuals towards the channel and produces a change in their initial perceptions (Venkatesh and Morris, 2000; Gefen et al., 2003; Yu et al., 2005), makes it necessary to study not only the first e-purchase, as in previous research, but also the ‘‘post-adoption” stages (Karahanna et al., 1999; Vijayasarathy, 2004). Consequently, research must also be directed towards the study of experienced e-shoppers who may become loyal customers of the channel. The objective of the present study is to analyse the e-purchasing behaviour of experienced e-shoppers, employing a dual perspective: (1) present e-purchasing behaviour and (2) future repurchasing behaviour measured through repurchasing intentions. To this end, we have formulated a model which applies metrics that have proven consistent and robust in previous studies and which broadens them by the inclusion of other factors that act as antecedents * Corresponding author. Tel.: +34 976 761 000x4944; fax: +34 976 761 767. E-mail addresses: [email protected] (B. Hernandez), [email protected] (J. Jimenez), [email protected] (M. Jose Martin). 0953-5438/$ - see front matter Ó 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.intcom.2008.11.001

of behaviour. The effects of experience with the Internet and perceptions such as self-efficacy, ease of use and usefulness on the two types of behaviour will be analysed. We will also study the inter-relationships between the explanatory variables. They are fundamentally obtained from the Technology Acceptance Model (TAM) (Davis, 1989), taking into account other models such as the Innovation Diffusion Theory (IDT) (Rogers, 1983, 1995) the Social Cognitive Theory (SCT) (Bandura, 1977, 1982, 1986), the Decomposed Theory of Planned Behaviour (Taylor and Todd, 1995) and the Task-Technology Fit Model (TTFM) (Goodhue, 1995; Goodhue and Thompson, 1995), which increase the capacity for analysis. These models have all been used in the Information Technology (IT) field and have contributed to the understanding of user behaviour. However, there is still room for improvement. Based on conceptual and empirical similarities of these theories, we have formulated a model, which we call the Model of Continued E-Commerce. The following section describes the theoretical approach and the formulation of hypotheses; subsequently, the methodology employed and the empirical analyses are presented. Finally, the study ends with a discussion of the results obtained, the conclusions and the implications. These findings should be of interest to an academic and a business audience. From a theoretical perspective, this study proposes a model which identifies the antecedents of present and future repurchasing behaviour and analyses a type of e-consumer which has scarcely been examined by previous research. Thus, as Olson and Olson (2003) recommend for the IT field, our study has concentrated on a specific segment, behaviour and context. From a practical viewpoint, the findings establish the most

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important perceptions of e-shoppers, once they have acquired experience with the channel; this should help companies to design more effective strategies to achieve customer loyalty.

2. Theoretical framework and hypotheses 2.1. E-purchasing behaviour In recent years, research has studied why individuals employ a certain IT, testing different variables to measure individual behaviour. The main final variables which represent consumer behaviour are focused either on the present use of an IT (present e-behaviour) (Achjari and Quaddus, 2003; Klopping and McKinney, 2004; Pikkarainen et al., 2004), or on the future use (intentions or future e-behaviour) (Tsai and Su, 2007; Shin, 2007, 2008). Frequent present use of a technology is considered a basic requisite in order to achieve all the benefits that may arise from that technology (Robey, 1979; Swanson, 1982; Davis, 1989; DeLone and Mclean, 1992). The main drawback of this variable is that it does not capture the viability of an IT in the medium and long term because use does not reflect user satisfaction. Consequently, neither does it reflect the development that the IT could achieve in the future. Some research has tried to resolve this problem, by analysing future use (Herrero and Rodríguez, 2008; Shin, 2008). Future use provides a reliable approximation of behaviour (Wang et al., 2003; Lu et al., 2005) and has already been used in seminal theories such as the Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975), and the Theory of Planned Behaviour (TPB) (Schiffer and Ajzen, 1985). The relationship that exists between present and future behaviour has been widely analysed in the classic research of consumer behaviour (LaBarbera and Mazursky, 1983), and its nature has been found to depend on the type of research carried out. Intentions explain the use made only if a longitudinal study is carried out (as we can see in Yi and Hwang, 2003), but never for transversal research (as state Van den Poel and Buckinx, 2005). For this kind of research, present behaviour (experience or habit) explains future behaviour (Goldsmith, 2002; Van den Poel and Buckinx, 2005). The meta-analysis carried out by Ouellette and Wood (1998) concludes that present behaviour must be considered as an additional key variable in explaining future behaviour. We consider that the high correlation between the two concepts makes it necessary to carry out a joint analysis which allows us to determine the nature of the relationship between them. This joint analysis extends the basic formulations of the behaviour models usually studied in the technology field. Present e-behaviour can be considered as both the habit that conditions future behaviour and as a barrier to change because of the non-transferable system learning acquired by the user (Keaveney and Parthasarathy, 2001). Consequently, for e-commerce, we will test whether present e-purchasing behaviour explains future intentions to continue purchasing on the Internet.  H1: Present e-purchasing behaviour directly and positively influences behaviour regarding future e-purchases. A third variable related to an individual’s IT behaviour is the prospective user’s overall attitude towards using an IT. It was included in the most important models as a predictor of user behaviour (TRA and TPB), and it was also tested both in the original formulation of TAM (Davis et al., 1989) and in subsequent papers (Chin and Gopal, 1995; Chau, 1996). On the basis of this research, attitude has become one of the key variables of IT acceptance (Chen and Tan, 2004; Schneberger et al., 2007/2008), especially in e-commerce (Herrero and Rodríguez, 2008).

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Attitude precedes and produces present and future e-purchasing behaviour, and it can be used to predict both of them (Keen et al., 2004; Tsai and Su, 2007). It seems logical to assume that the more positive the attitude to e-purchasing, the greater will be the number of purchases made and the greater will be the willingness to continue buying over the Internet in the future (Achjari and Quaddus, 2003; Castañeda et al., 2007). On the contrary, unfavourable attitudes are expected to cause the user to stop using the new channel for e-purchasing (Liker and Sindi, 1997). We consider attitude as an antecedent of behaviour (Vijayasarathy, 2004) and can be used as a proxy of behaviour. Consequently, attitude has a close relation with present and future e-purchasing behaviour. Thus, we have formulated the following hypotheses:  H2: The attitude of individuals to e-purchasing positively influences behaviour regarding future e-purchases.  H3: The attitude of individuals to e-purchasing positively influences behaviour regarding present e-purchases. Below, we are going to analyse the antecedents that explain Internet purchasing behaviour. To do so, we follow the above-mentioned models. 2.2. Perceived usefulness and ease of use The TAM is an adaptation of TRA, which explains almost every type of human behaviour on the basis of beliefs and intentions (Fishbein and Ajzen, 1975). TAM concentrates exclusively on the analysis of IT and establishes, a priori, two key perceptions: ease of use (PEOU) and usefulness (PU) (Davis, 1989; Davis and Wiedenbeck, 2001; Featherman and Pavlov, 2003). In spite of its simplicity, this type of model has been employed in a wide range of research and has been demonstrated to give great explanatory power (see the bibliographical review carried out by Lee et al., 2003). PU is the degree to which users consider that the use of a specific IT improves their results (Davis, 1989; Lederer et al., 2000). This idea leads us to the term ‘‘relative advantage” proposed by Rogers (1983, 1995) as one of the five key elements of his IDT, which states that an innovation is more rapidly diffused if it is perceived as a source of value. Likewise, when users perceive that an IT is very useful, they believe that it offers a positive ‘‘use-performance” relationship. PEOU reflects the perception that the use of an IT does not require additional effort (Davis, 1989). This concept is related to features of IT such as easily understandable functions and contents, ease of learning or simplicity of use. It is inversely related to perceived complexity, previously proposed by Rogers (1983, 1995). It is assumed that PEOU and PU are the major influences on individuals’ attitudes towards using IT (Shin, 2008). As a result, these perceptions explain the technological behaviour of individuals both directly and indirectly. Both perceptions influence present purchasing behaviour directly (Lu et al., 2005; Luarn and Lin, 2005) because the greater the perceived usefulness and ease of use, the greater will be the number of exchanges carried out. Indirectly, PEOU and PU condition users’ attitude to e-purchasing so they are influencing future repurchasing behaviour (Davis et al., 1989; Chau and Hu, 2001; Chen and Tan, 2004). On the basis of these ideas, we formulate the following hypotheses:  H4: The usefulness perceived by individuals their present purchasing behaviour.  H5: The ease of use perceived by individuals their present purchasing behaviour.  H6: The usefulness perceived by individuals their attitude to e-purchasing.  H7: The ease of use perceived by individuals their attitude to e-purchasing.

positively influences positively influences positively influences positively influences

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We have summarised some of the most important papers about IT acceptance in Appendix B. They test, at least, one direct relation between PEOU and PU and the final variables analysed (present and future e-behaviour and attitude). From this Appendix, attitude can be seen to be a variable that is determined by purchasing perceptions and conditions final behaviour, both present and future. It is interesting to point out that, for the review of the literature carried out, future behaviour is the final variable most widely analysed and the one that obtains the best results. On the contrary, the relation between attitude and present e-purchasing behaviour has hardly been tested. It is also interesting to observe the strong influence of PU on the other variables tested in comparison with PEOU. As Shin (2007) argues, the evolution of the Internet must drive researchers to modify basic frameworks of analysis in order to provide empirical evidence applicable to the new situation. We consider that there is an obvious need to mix different streams of research so as to capture a greater number of nuances than are not captured by the initial formulations of the models. As a result, our study has tested additional variables derived from different models, producing a more comprehensive analysis that aims to reflect e-purchasing behaviour more accurately. 2.3. Perceived self-efficacy and experience with the Internet The broadening of IT acceptance models in recent years has included complementary perceptions which either act like PEOU and PU (Liu and Wei, 2003; Shin, 2007) or operate as intermediaries between them and the final variable, whether this is present or future behaviour (Van der Heijden and Verhagen 2004; Roca et al., 2006; Shin, 2008). In contrast to this research, our work extends the framework studied by analysing, in greater depth, prior external variables (a term proposed by Davis et al., 1989) which condition PEOU and PU. In other words, we have used other behavioural theories as a basis and included factors related to the internal motivations of individuals, such as self-efficacy (Chen et al., 2002; Bruner and Kumar, 2005) or experience with the Internet (Shim et al., 2001; Liaw and Huang, 2003; O’Cass and Fenech, 2003). Perceived self-efficacy was initially defined as the ‘‘belief of the individual in his/her own capacity to effect a specific behaviour” (Bandura, 1977, 1982), and it was later redefined by Ajzen (1985, 2002) for the study of ‘‘perceived control”. The close relation between control and self-efficacy has led many researchers to generate a global average to cover the two perceptions (for example, Sparks et al., 1997). This new variable reflects the individual’s feeling about his/her capacity to carry out a certain action. It has been analysed by behaviour models such as the SCT (Bandura, 1977) and the TPB (Schiffer and Ajzen, 1990), and was later incorporated by the decomposed version of this theory applied exclusively to the IT field (Taylor and Todd, 1995). Most research that has analysed the concept of self-efficacy has highlighted its importance in the development of the individual’s present and future e-behaviour (Compeau and Higgins, 1995; Koufaris and Hampton-Sosa, 2002; Lim et al., 2007). Based on SCT, it could be affirmed that self-efficacy influences (1) decisions about what behaviour to undertake, (2) the effort necessary for this behaviour and (3) the individual’s performance. Research into self-efficacy has focused on the direct effect of this concept on final behaviour (e.g., Compeau and Higgins, 1995; Limayem et al., 2000; Wang et al., 2003), forgetting that this effect can be caused through other mediating variables. With regard to e-commerce and self-efficacy, individuals must feel themselves capable of managing and controlling the technology during the purchasing act (Chau and Hu, 2001). Following the definition of Marakas et al. (1998) for computers, this study considers that the e-commerce self-efficacy describes individuals’

ability to apply their skills to complete a purchase on the Internet. To the best of our knowledge, the study of this variable in the e-commerce arena has been scarce and not very consistent. In this context, our research contributes an approach that it is not habitual in the literature (e.g. Yi et al., 2006; Wu et al., 2007). It proposes that perceived self-efficacy exerts a direct effect on the other perceptions of the e-shopper (usefulness and ease of use) and an indirect effect on final e-purchasing behaviour. Consequently, we have included the following hypotheses:  H8: The self-efficacy perceived by individuals positively influences the perception of ease of use regarding e-purchasing.  H9: The self-efficacy perceived by individuals positively influences the perception of usefulness regarding e-purchasing. Bandura (1986) argues that perceived self-efficacy is explained by the direct experience gained previously by the individual. This experience is the most influential source of information and refers to the abilities acquired through interaction with the Internet (Compeau and Higgins, 1995; Koufaris, 2002; Koufaris and Hampton-Sosa, 2002). The technological experience was included in, amongst other theories, the TTFM (Goodhue, 1988; Teo et al., 1999), the model proposed by Triandis (1980) and the SCT (Bandura, 1977). Experience facilitates the acquisition of information and increases individuals’ knowledge, which modifies users’ initial perceptions (Venkatesh and Davis, 2000; Min and Galle, 2003) and encourages the adoption of other information technologies (Thompson et al., 1994; Teo et al., 1999). It appears logical to assume that users with more experience in navigating, who access the Internet more frequently and, in addition, are satisfied with it, have a greater perception of self-efficacy and are more inclined to favour e-purchasing (Zhang and Von Dran, 2000; Goldsmith, 2002; Yoon et al., 2002). Following this research, our study measures experience through three concepts: acceptance of the Internet, frequency of use and satisfaction1. We wish to demonstrate that the perception of self-efficacy is stronger for individuals who accept the Internet, are frequent users and, furthermore, whose past experiences have been satisfactory. Thus, we have formulated the following hypotheses:  H10: Prior acceptance of the Internet by individuals positively influences their perception of self-efficacy.  H11: The frequency of use of the Internet by individuals positively influences their perception of self-efficacy.  H12: The satisfaction experienced by individuals on the basis of their prior experiences with the Internet positively influences their perception of self-efficacy. The three factors are reflections of experience and allow us to know the basic requirements that must be fulfilled for an individual to carry out an Internet purchase. They are inter-linked because they reflect Internet behaviour from different perspectives, one of which is objective, i.e. frequency of use (Lohse et al., 2000; Tan and Teo, 2000; Shih, 2004) and the others subjective, acceptance and the satisfaction gained from the Internet (Gelderman, 1998; Bhattacherjee, 2001). Objective and subjective measures are not always equivalent because, as Straub et al. (1995) state, the link between them depends on each individual and the precision of his/her perceptions about use. Some research has theoretically proposed the relationships between them but they have hardly

1 Satisfaction has been included on the basis of the Expectation-Confirmation Theory (Oliver, 1980; Bearden and Teel, 1983). This variable reflects the performance which individuals consider they have obtained, measures the success achieved during past interactions and influences individuals’ subsequent behaviour (Delone, 1988; Soh et al., 1992).

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ACCEPTANCE OF THE INTERNET

EASE OF USE OF EPURCHASING

H10

H7

H13 H8 FREQUENCY OF INTERNET USE

H11

H5

PERCEIVED SELFEFFICACY

H2

H6

H9

H12

FUTURE REPURCHASING BEHAVIOUR

H3 H1

H14 SATISFACTION WITH THE INTERNET

EPURCHASING ATTITUDE

PRESENT E-PURCHASING BEHAVIOUR

USEFULNESS OF EPURCHASING

H4

Fig. 1. The Model of Continued E-Commerce.

been tested empirically. We, therefore, propose the following hypotheses:

3.2. Preliminary analyses

3. Analysis of the information

Initial reliability studies eliminated all indicators with an itemtotal correlation lower than 0.3 (Nurosis, 1993), or whose Cronbach’s alpha did not exceed the reference value of 0.6. Employing these criteria, item ACEP2 did not reach the recommended limits and was eliminated. Following this initial refining, the results obtained displayed clearly satisfactory values in all cases (Table 1). The unidimensionality of the scales was analysed via an exploratory factor analysis, applying, where necessary, varimax rotation with Kaiser normalisation (Kaiser, 1974). The results achieved were adequate (Table 1).

3.1. Methodology

3.3. Confirmatory analysis

The data was obtained through a survey carried out using the CATI technique. In order to guarantee the representativeness of the population, the random quota sampling method was employed, according to criteria of age, gender and geographical location. A pre-test was carried out to correct possible defects and to foresee the doubts and problems of interviewees during the data collection process. A total of 2615 telephone calls were made; after refining, the final sample contained 225 experienced e-shoppers. Most of the factors were measured using 7-point Likert scales (Appendix A). The sole exception was the factor Present e-purchasing behaviour, which was measured on the basis of the number of e-purchases the individual have made (Hubona and Kennick, 1996; Novak and Hoffman, 1997; Klopping and McKinney, 2004). The items included in the survey for the factors Acceptance of the Internet, PEOU, PU, Attitude and Future repurchasing behaviour were those which have most commonly been used in previous TAM studies (Davis, 1989; Davis et al., 1989; Karahanna et al., 1999; Vijayasarathy, 2004; Yu et al., 2005). Items of Satisfaction with the Internet are derived from Spreng et al. (1996), Bhattacherjee (2001), and Shih (2004). We should especially mention that the factor Perceived Self-efficacy has been measured following the papers of Compeau and Higgins (1995), Limayem et al. (2000), and Koufaris and Hampton-Sosa (2002), amongst others. The items used are related to the individual’s capacity for searching for and shopping for products on the Internet. Subsequently, the process of refining the scales was structured in various stages, starting with the preliminary analyses2 and continuing with a confirmatory analysis.

The second phase of scale validation consisted of carrying out a confirmatory factor analysis (Hair et al., 1999). To this end, the structural equation method (SEM) was applied, using EQS 6.1 statistical software, recurring to the robust maximum likelihood estimation method, since our data do not fulfil the hypothesis of normality (Chou et al., 1991; Bentler, 1995). Subsequently, we progressively eliminated the indicators which did not meet the criteria proposed by Jöreskog and Sörbom (1993): weak convergence (Steenkamp and Van Trijp, 1991), strong convergence (Steenkamp and Van Trijp, 1991) and the explanatory coefficient (R2 < 0.3). All our indicators reached acceptable values according to the three criteria and, thus, the following step was to test whether the goodness of fit indices exceeded the optimum levels recommended by Hair et al. (1999) (Table 2). Finally, the reliability and validity of the constructs were studied (Churchill, 1979). Reliability, initially measured using Cronbach’s alpha, was verified by the Composite Reliability Coefficient (CRC) (Jöreskog, 1971). All the factors attained the recommended limit of 0.6 (Bagozzi and Yi, 1988). With regard to validity, a distinction was made between convergent and discriminant validity. The former tests the convergence between the items and their corresponding construct; standardised values of the significant loadings and which exceed 0.53 should be obtained (Steenkamp and Van Trijp, 1991). For the test of discriminant validity we calculated the confidence interval between different factors and verified that none of them contain the value 1 (Table 2). Thus, we can confirm the existence of validity in the measurement model proposed.

 H13: Prior acceptance of the Internet by individuals positively influences their frequency of use.  H14: Prior acceptance of the Internet by individuals positively influences the satisfaction they feel. All these relationships can be observed in the model presented in Fig. 1.

2

The statistical package used was SPSS/PC, version 14.0 for Windows.

3

Significant at 0.01.

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Table 1 Preliminary analyses.

ACEP SATIS SEF PEOU PU ATT FUT

Cronbach’s alpha

% Explained variance

Loadings

Factors

0.597/0.637 0.722 0.709 0.761 0.837 0.875 0.844

52.8%/69.3% 78.84% 64.12% 70.32% 75.50% 80.34% 70.32%

>0.5 >0.5 >0.5 >0.5 >0.5 >0.5 >0.5

1 1 1 1 1 1 1

3.4. Structural analysis and results The next step was to analyse the causal relationships proposed in the Model of Continued E-Commerce using structural equation modelling. The goodness of fit indices reach the limits recommended by Hair et al. (1999): GFI = 0.870; RMSR = 0.067; RMSEA = 0.071; NNFI = 0.902; IFI = 0.918; CFI robust = 0.947; v2 normed = 2.12. The results confirm nine of the 14 hypotheses proposed, since their coefficients are significant and positive (Fig. 2). We can see that future e-purchases are determined by present epurchasing behaviour and by the attitude of the shopper towards the online channel. Consequently, H1 and H2 are fulfilled. However, attitude does not explain present e-purchasing behaviour, so H3 is rejected. This is in line with the research cited in Appendix B, where it can be observed that this relation has hardly been tested. PU explains the attitude of the shopper and present e-purchasing behaviour, while PEOU does not have a significant effect upon either of the two variables. The greater the perceived usefulness, the greater the number of exchanges completed and the more positive is the attitude of the shopper towards e-purchasing. Thus, usefulness is the principal explanatory variable of the Internet purchasing process, both present (b4 = 0.564) and future (its overall influence is 0.67). Our results show that ease of use ceases to be an important factor in the analysis of experienced consumers’ behaviour, since they base their decisions upon perceived

Table 2 Confirmatory factor analysis. CRC

Factors

Intervals

Factors

Intervals

ACEP

0.692

SATIS

0.787

SEF

0.765

PEOU

0.889

PU

0.854

ATT

0.906

FUT

0.860

ACEP–SATIS ACEP–SEF ACEP–PEOU ACEP–PU ACEP–ATT ACEP–FUT ACEP–FREQ SATIS–SEF SATIS–PEOU SATIS–PU SATIS–ATT SATIS–FUT SATIS–FREQ SEF–PEOU

(0.62–0.93) (0.68–0.98) (0.27–0.62) (0.48–0.83) (0.45–0.79) (0.13–0.51) (0.01–0.38) (0.36–0.68 (0.17–0.54) (0.13–0.56 (0.18–0.59) (0.17–0.49 ( 0.17–0.14) (0.58–0.82)

SEF–PU SEF–ATT SEF–FUT SEF–FREQ PEOU–PU PEOU–ATT PEOU–FUT PEOU–FREQ PU–ATT PU–FUT PU–FREQ ATT–FUT ATT–FREQ FUT–FREQ

(0.61–0.84) (0.59–0.81) (0.33–0.62) (0.11–0.45) (0.33–0.60) (0.39–0.64 (0.22–0.50) (0.14–0.46) (0.88–0.98) (0.52–0.74) (0.00–0.31) (0.52–0.75 ( 0.09–0.20) (0.08–0.38)

Absolute fit

Incremental fit

Parsimony fit

Goodness of Fit (GFI) = 0.89; root mean square residual (RMSR) = 0.06; root mean square error of approximation (RMSEA) = 0.067

Non NFI = 0.92; Incremental fit index (IFI) = 0.94; comparative fit index (CFI) robust = 0.96

v2 normed = 2.00

usefulness. H4 and H6 are confirmed, while H5 and H7 are rejected. We should underline the important role of self-efficacy as an antecedent of the perceptions linked to e-purchasing (ease of use and usefulness); H8 and H9 are verified. Self-efficacy is explained by previous experience with the Internet, measured by acceptance (b10 = 0.874) and frequency of use (b11 = 0.185). H10 and H11 are confirmed. However, satisfaction with the Internet does not influence perceived self-efficacy, so H12 is rejected. Acceptance of the Internet does not affect frequency of use, but it does influence satisfaction with the Internet (b14 = 0.737). H13 is rejected and H14 is confirmed. Thus, shoppers who more readily accept the Internet are more satisfied with their past experience and also perceive greater self-efficacy during their actions. Finally, the proposed model achieves an explanatory coefficient which exceeds 51% for future e-purchases and 46% for present e-purchasing, while the figure for attitude, considered by some authors as the predisposition to purchase, is 87%. 4. Discussion The results obtained demonstrate that the fact of having acquired a product via the Internet positively and directly influences future repurchasing behaviour, thereby verifying the relationship which exists between the present and future behaviour of e-shoppers. These results contribute to clarifying a causal relation that has hardly been tested in cross-sectional studies (see some exceptions in Goldsmith, 2002, and Cho, 2004). In this line, other research, such as that of Mowen and Minor (1998), already determined that the feeling of satisfaction derived from a positive past experience increases the probability of this conduct being repeated. Our results verify this relation for the case of e-purchasing behaviour. As for attitude, our research demonstrates that e-shoppers with a more positive attitude show a greater intention to continue purchasing products online, although they do not modify their present behaviour. These results are contrary to the proposal of Davis (1993) and are probably due to the fact that the formation of an individual’s attitude in cross-sectional studies occurs simultaneously to the development of his/her present e-purchasing behaviour. Therefore, although there is a high correlation between the two concepts, the causal relation proposed cannot be verified. With respect to the factors that determine attitude, usefulness is the most important perception for the present and future repurchasing behaviour. These results are coherent with most TAM studies (see Appendix B), which consider that usefulness may reflect considerations of the ‘‘benefits” of using the target system and the expected positive relationship between use and outcomes. Consequently, once the barrier of adoption has been overcome, usefulness is the perception that most influences the attitude of experienced users. Perceived ease of use does not have a significant effect on either of the two behaviours (present and future) analysed. These results are coherent with the initial formulation of the TAM proposed by Davis (1989), who tested his model on a sample of users ‘‘unfamiliar with the systems used in the study”. His results showed that PEOU lost its significance once the users had acquired experience and knowledge of the system (after 14 weeks using the IT). Therefore, our results are probably due to the e-commerce experience of the sample analysed. We can find similar results in papers about TAM, such as Chau (1996) and Gefen and Straub (2000). The former states that the effect of PEOU is only important in the short term whilst the latter demonstrated that its influence on future purchase is not significant.

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ACCEPTANCE OF THE INTERNET EASE OF USE OF EP

0.874 (3.63)

0.107 (1.62)

0.124 (1.52) 0.04 (0.43)

0.680 (5.58) FREQUENCY OF INTERNET USE 0.737 (7.08)

0.185 (2.23)

PERCEIVED SELFEFFICACY

-0.273 0.88 (8.46)

0.714 (8.69)

-0.141 (-0.56)

EP ATTITUDE 0.55 (6.9)

(-0.76) 0.326 (4.98)

USEFULNESS OF EP

SATISFACTION WITH THE INTERNET

0.564 (4.2)

FUTURE REPURCHASING BEHAVIOUR

PRESENT EP BEHAVIOUR

EP: E-purchasing; Significant relationship;

Non significant relationship

Fig. 2. Results obtained by the Model of Continued E-Commerce (standardised coefficient and its t-statistic in brackets).

As Karahanna et al. (1999) state, ‘‘as users gain experience with the system, ease of use concerns seem to be resolved and displaced by more instrumental considerations involving the efficiency of the innovation to increase one’s job performance (i.e. perceived usefulness)” (p. 200). Our Model of Continued E-Commerce permits the analysis of the factors which precede PEOU and PU and, consequently, shopping behaviour. The findings suggest that users who consider that they have more competence and capacity also have better perceptions about e-commerce and, as a consequence, carry out more online purchases. As in the paper of Wu et al. (2007) and Ong et al. (2004), self-efficacy acts as an antecedent and has an indirect influence on final behaviour. Therefore, PEOU and PU channel the influence of self-efficacy and act as mediating variables. It is important to highlight that the overall influence of self-efficacy on present and future repurchasing behaviour is 0.403 and 0.477, respectively. Our findings demonstrate that previous experience with the Internet (acceptance and frequency of use) is a prerequisite for individuals to feel more confident during their purchasing acts. As a result, both variables must be included in order to predict e-purchasing behaviour because previous general use of the Internet conditions specific applications of this IT, such as e-commerce. Finally, concerning the relationship between acceptance of the Internet, frequency of use and satisfaction, we must emphasise the significant effect of acceptance upon satisfaction, and the rejection of the relationship between acceptance and frequency. These results are similar to those obtained by other studies (Barnett et al., 2006/2007), which demonstrate that the subjective and objective variables employed to measure experience of an IT do not necessarily obtain the same results. 5. Conclusions, implications and limitations 5.1. Conclusions The main objective of this paper has been to study the present and future e-purchasing behaviour of experienced e-shoppers, who are the great unknown for many online businesses. The experience derived from e-purchasing produces a change in their initial perceptions which makes it necessary to study their behaviour in

post-adoption stages. We have formulated a model of continued e-commerce and included variables obtained from various behavioural theories linked to IT. This approach has permitted us to study repeat purchasing behaviour in greater depth and, consequently, to analyse the most important perceptions for experienced e-shoppers. One of the most revealing conclusions of our paper is the definition of future e-purchasing behaviour as a function of present behaviour. Individuals who have purchased more times are more inclined to continue purchasing online, which appears to indicate that consumer satisfaction stems from the experience obtained from purchasing. If the individual has made at least one exchange, the most reasonable result is that the action tends to be repeated, rather than remaining as an isolated act. Consequently, users’ habits are a key explanatory variable in models related to IT. With respect to the relation that is established between attitude and purchasing behaviour, we conclude that its significance depends on the moment at which the variables have been measured and on the temporal sequence between them. E-purchasing behaviour is determined by intention and attitude, which occurred previously. Therefore, in studies in which the variables have been observed at the same moment, attitude towards e-commerce does no explain purchases already carried out. The increase in efficiency, the greater convenience or the increase in the speed of purchase (aspects which are inherent to usefulness) improve individuals’ attitude towards this channel and strengthens their predisposition to purchase. This is because the principal motivations for e-purchasing are extrinsic and utilitarian. Perceived ease of use (or more appropriately its opposite, effort of use) may be seen as part of the cost of using an IT (Davis, 1993). This cost acts as a brake during the initial stages of e-purchasing because new shoppers may be influenced by the effort they have to make to use the IT. However, this does not condition the behaviour of experienced e-shoppers. Therefore, when users have extensive knowledge of the IT, PEOU is no longer a crucial perception in the study of their e-behaviour. As Keil et al. (1995) concluded, ‘‘no amount of ease of use will compensate for low usefulness” (p.89). With regard to self-efficacy, we can affirm that it is fundamental for the development of e-purchasing and determines

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the other perceptions and behaviour linked to purchasing online. As the Decomposed Theory of Planned Behaviour (Taylor and Todd, 1995) and SCT (Bandura, 1977, 1986; Compeau and Higgins, 1995) affirmed for other IT, self-efficacy stimulates consumers to behave more efficiently and to undertake further actions that would seem risky if they lacked confidence in themselves. Finally, Internet experience is necessary to develop other more specific e-applications. Developing a state of acceptance and a frequency of Internet use makes the user feel safer and more capable of shopping online, thus improving his/her perceptions and behaviour. 5.2. Implications Our study has important implications for both research and business. For the academic audience, we should like to highlight three important contributions. Firstly, the Model of Continued E-Commerce contributes to the study of experienced Internet shoppers’ behaviour. Previous studies have formulated wide models for the analysis of e-commerce without distinguishing between non-purchasing and experienced shoppers, as they considered that the low level of e-commerce prevented their differentiation. However, in recent years e-commerce has expanded enormously and, so, many users have made numerous exchanges and are familiar with its functioning. Experienced eshoppers gain information in a variety of forms (Cyr et al., 2007) and take purchasing decisions following a decision-making process different to that of new e-shoppers. Our study, therefore, focuses exclusively on experienced e-shoppers, thereby defining the behavioural pattern of the target market that e-businesses must aim at. Secondly, our model is one of the few studies which combines different theories (TAM, IDT, TPB, SCT and TTFM) and simultaneously analyses the antecedents of two types of e-purchasing behaviour: present and future. Present e-behaviour with respect to an IT is a good predictor of future behaviour. Therefore, inclusion of testing of the behaviour of experienced users would help to improve the explanatory power of the IT acceptance models. Finally, previous research about TAM has usually been based on the idea that PEOU and PU are the most important determinants of behavioural intentions with respect to new IT. Our model has demonstrated that the influence of PEOU is not important for experienced e-shoppers in the post-adoption stages. Consequently, following Bhattacherjee (2001), we consider that future research based on TAM should adapt the basic structure proposed by Davis (1989) accordingly. As for the managerial implications, our results allow us to know which perceptions firms should encourage to improve present and future e-purchasing behaviour, and thus achieve long-term relations with their customers. The perceptions of the experienced eshopper are not necessarily the same as those that determine the first purchase. Therefore, to know the e-purchasing behaviour of experienced shoppers is fundamental to design more effective strategies so that they repurchase and become loyal to the channel. This is what distinguishes successful ventures from failed attempts (Cyr et al., 2007). The aim of an e-business is to optimise interaction with the customer via the Internet and thereby overcome some of the barriers inherent to the channel, such as the lack of a bricksand-mortar establishment and personal service. Therefore,

e-businesses must invest in order to improve their performance and to offer greater added value on their websites compared to the physical market. To offer this added value, firms that sell through the Internet must improve the perceptions of self-efficacy and usefulness because they are the most important ones for experienced shoppers. Internet firms must adequately communicate the advantages and properties of e-commerce in general in an attempt to make shoppers feel capable of performing any type of e-exchange correctly. Furthermore, it would be helpful to increase users’ familiarity with the Internet in order to improve their self-efficacy. Lastly, firms should provide services and information unavailable on other purchasing channels. This will increase users’ perceptions of the efficiency and usefulness of e-commerce. 5.3. Limitations and future research lines Firstly, we must point out the existence of a possible limitation derived from the characteristics of the sample analysed. Experienced e-shoppers know the Internet as a shopping channel and, thus, they have already accepted e-commerce. Therefore, the explanatory power of the model for present and future e-purchasing behaviour may be partially due to the type of e-customer analysed and not only to the effect of the perceptions included in the model. The second limitation of this study is related to the existence of other relevant underlying variables not included in the model. Some of the analysed perceptions, such as usefulness, may contain the effect of these underlying variables, acting as a proxy for them and as a key driver for the development of e-commerce. Nevertheless, if the underlying variables were included in the model, the values we have found of the effect of the perceptions analysed might be diminished. In future research, we want to test the effect of underlying variables connected with e-purchasers’ habits and with their social environment on e-purchasing behaviour. Thirdly, it must be highlighted that the purchasing behaviour analysed is approached from a general perspective of e-purchasing which does not take into account the product acquired (goods or services) or features of the user interface. Product type may modify the behaviour and perceptions of users because they perceive different risks depending on its cost and level of tangibility. Moreover, additional features included in the interface of the website (e.g. accessibility, navigability, etc) would influence users’ experience and e-behaviour. Consequently, future research will attempt to distinguish between the types of products acquired, the different features of webpage interfaces and the perceptions which influence the finalisation of each exchange. A final limitation stems from the cross-sectional nature of the study, which does not permit us to analyse the evolution of shopping behaviour. Thus, future research will be aimed at establishing, via a longitudinal study, whether the future behaviour analysed here is supported by the number of purchases subsequently carried out. Acknowledgements The authors wish to express their gratitude for the financial support received from the Spanish Government CICYT (SEJ2005/05968), and the Aragón Regional Government (Generés S-09).

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Appendix A. Scales employed Factor

Indicators

Acceptance

My general opinion of the Internet is positive Using the Internet is easy for me The Internet seems useful to me The experience I have had with the Internet has been satisfactory In general, I am satisfied with the service provided by the Internet How often do you use the Internet? I feel capable of using the Internet for purchasing products I feel capable of locating shopping sites on the Internet I feel comfortable searching for information about a product on the Internet Learning to use the Internet for shopping was easy for me Using the Internet to purchase a product does not require a lot of mental effort The Internet would be easy to use to make my purchases Using the Internet to acquire a product would permit me to purchase more efficiently Using the Internet to acquire a product would permit me to purchase more quickly Using the Internet to acquire a product would be useful to make my purchases Using the Internet to make my purchases is a good idea My general opinion of e-commerce is positive Using the Internet to purchase a product seems an intelligent idea to me I am likely to purchase a product over the Internet (in the near future) It is likely that the Internet will be the medium I use to make my purchases in the future I intend to use the Internet to purchase a product in the near future Number of e-purchases made

Satisfaction

Self-efficacy

Perceived Ease of Use

Perceived Usefulness

Attitude

Future repurchasing

Present purchasing

ACEP1 ACEP2 ACEP3 SATIS1 SATIS2 FREQ SEF1 SEF2 SEF3 PEOU1 PEOU2 PEOU3 PU1 PU2 PU3 ATT1 ATT2 ATT3 FUT1 FUT2 FUT3 CCA

Appendix B. Summary of relationships tested in previous studies in IT acceptance (1993–2008) Study

PU-ATT

PU-Present e-behaviour

PU-Future e-behaviour

PEOU-ATT

PEOUFuture e-behaviour

ATT-Future e-behaviour

PEOUPresent e-behaviour

ATT-Present e-behaviour

Davis (1993) Chin and Gopal (1995) Chau (1996)

SIGNIFICANT SIGNIFICANT

SIGNIFICANT –

– –

SIGNIFICANT –

– SIGNIFICANT

– –

– –

SIGNIFICANT –

SIGNIFICANT

–

–

–

–

–

–

Hu et al. (1999)

SIGNIFICANT

–

SIGNIFICANT

SIGNIFICANT

–

–

Gefen and Straub (2000) Lederer et al. (2000) Bhattacherjee (2001) Chau and Hu (2002) Davis and Wiedenbeck (2001) Chau and Hu (2001) Achjari and Quaddus (2003) Gefen et al. (2003) Featherman and Pavlov (2003) Wang et al. (2003)

–

–

SIGNIFICANT

NON SIGNIFICANT –

NON SIGNIFICANT –

–

SIGNIFICANT

–

–

–

SIGNIFICANT

–

–

–

–

NON SIGNIFICANT –

–

SIGNIFICANT

–

SIGNIFICANT

–

–

–

–

–

–

SIGNIFICANT

–

SIGNIFICANT

–

–

–

NON SIGNIFICANT

–

NON SIGNIFICANT –

–

–

SIGNIFICANT

–

SIGNIFICANT

–

SIGNIFICANT

–

SIGNIFICANT

–

–

SIGNIFICANT

–

–

–

NON SIGNIFICANT –

–

–

SIGNIFICANT

–

–

SIGNIFICANT

–

SIGNIFICANT

–

–

–

–

–

SIGNIFICANT

–

–

–

–

–

–

SIGNIFICANT

–

NON SIGNIFICANT SIGNIFICANT

–

–

–

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Appendix B (continued) Study

PU-ATT

PU-Present e-behaviour

PU-Future e-behaviour

PEOU-ATT

PEOUFuture e-behaviour

ATT-Future e-behaviour

PEOUPresent e-behaviour

ATT-Present e-behaviour

Yi and Hwang (2003) Chen and Tan (2004) Klopping and McKinney (2004) Ong et al. (2004) Pikkarainen et al. (2004) Van der Heijden and Verhagen (2004) Vijayasarathy (2004) Luarn and Lin (2005) Lu et al. (2005) Thong et al. (2006) Castañeda et al. (2007) Shin (2007) Tsai and Su (2007) Herrero and Rodríguez, 2008 Schneberger et al. (2007/ 2008) Shin (2008)

–

–

SIGNIFICANT

–

SIGNIFICANT

–

–

–

SIGNIFICANT

–

SIGNIFICANT

SIGNIFICANT

–

SIGNIFICANT

–

–

–

SIGNIFICANT

SIGNIFICANT

–

SIGNIFICANT

–

–

–

SIGNIFICANT

–

–

SIGNIFICANT

–

SIGNIFICANT

–

–

–

SIGNIFICANT

–

–

–

–

–

–

–

SIGNIFICANT

–

SIGNIFICANT

–

NON SIGNIFICANT –

–

SIGNIFICANT

–

SIGNIFICANT

–

–

–

NON SIGNIFICANT –

–

–

NON SIGNIFICANT SIGNIFICANT

SIGNIFICANT

–

–

–

– –

– –

SIGNIFICANT SIGNIFICANT

– –

SIGNIFICANT SIGNIFICANT

– –

– –

– –

SIGNIFICANT

–

SIGNIFICANT

SIGNIFICANT

–

SIGNIFICANT

–

–

SIGNIFICANT –

– SIGNIFICANT

SIGNIFICANT SIGNIFICANT

– –

SIGNIFICANT –

– –

– –

SIGNIFICANT

–

–

NON SIGNIF

– NON SIGNIFICANT –

SIGNIFICANT

–

–

SIGNIFICANT

–

–

–

–

–

–

–

SIGNIFICANT

–

SIGNIFICANT

SIGNIFICANT

SIGNIFICANT

SIGNIFICANT

–

–

–, not tested.

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