Web Acceptance and Usage Model: A Comparison between Goal-directed and Experiential Web Users

Author Marketing Professor, Manuel J. Sánchez-Franco, Ph. D. Business Professor, José L. Roldán, Ph. D. Title Web Acceptance and Usage Model: A Comparison between Goal-directed and Experiential Web Users Address: English Spanish Business Administration Faculty University of Seville Avda. Ramón y Cajal, nº 1 41018-Seville Spain Facultad de Ciencias Económicas y Empresariales Departamento de Administración de Empresas y Marketing Universidad de Sevilla Avda. Ramón y Cajal, nº 1 41018-Sevilla España Professor Sánchez-Franco's research efforts focus on Internet marketing strategy, consumer behaviour in online environments, and psychological processes and Web advertising effects. Professor Roldán's research efforts focus on executive information systems (EIS), information system effectiveness, knowledge management, and partial least squares. Mail-to: [email protected] [email protected] Phone: 0034.954.55.96.68 / 0034.954.55.44.58 Fax: 0034.954.55.69.89 2 Abstract In this paper we analyse the Web acceptance and usage between goal-directed users and experiential users, incorporating intrinsic motives to improve the particular and explanatory TAM value –traditionally related to extrinsic motives-. A field study was conducted to validate measures used to operationalize model variables and to test the hypothesised network of relationships. The data analysis method used was Partial Least Squares (PLS). The empirical results provided strong support for the hypotheses, highlighting the roles of flow, ease of use and usefulness in determining the actual use of the Web among experiential and goal-directed users. In contrast with previous research that suggests that flow would be more likely to occur during experiential activities than goal-directed activities, we found clear evidence of flow for goal-directed activities. In particular the study findings indicate that flow might play a powerful role in determining the attitude towards usage, intention to use and, in turn, actual Web use among experiential and goal-directed users. Keywords: TAM, flow, usefulness, ease of use, enjoyment, experiential behaviour, goal-directed behaviour 3 INTRODUCTION Few studies focus directly (1) on Web acceptance and usage adopting a user-centred perspective, and (2) on the motives that affect behaviour. In fact, Novak et al. (2000) suggest that among marketing academics and Internet practitioners alike, there is a lack of genuine knowledge about the factors that bring about effective interactions with online customers. More recently, Parasuraman and Zinkhan (2002) point out that there is a considerable knowledge gap between the practice of online marketing and the availability of sound, research-based insights and principles for guiding that practice. In this situation of development, a model based on TAM (Technology Acceptance Model) and flow (essentially defined as an intrinsically enjoyable experience), is proposed to describe the main motives that (1) affect Web acceptance and usage and (2) make using the Web a compelling customer-experience. The purpose of this study is thus to reveal whether there exists the relation between flow and TAM-beliefs on the Web, and how the flow impacts the attitude and intention to use Web under a theoretically-based model. On the one hand, not everyone has agreed that extrinsic motives (e.g. how useful the technology would be) are sufficient. Over the years, there is a growing significant body of theoretical and empirical research regarding the importance of the role of intrinsic motives (e.g. how enjoyable the technology would be) in understanding facets of behaviour (e.g., Bagozzi et al., 1999; Eastlick and Feinberg, 1999; Holt, 1995; Hopkinson and Pujari, 1999; Sherman and Mathur, 1997). Specifically, there is a significant body of theoretical and empirical evidence regarding the importance of the role of intrinsic motives in IT (Information Technologies) acceptance and use (see Davis et al., 1992; Malone, 1981; Venkatesh and Speier, 1999, 2000; Webster and Martocchio, 1992). There is thus the need for incorporating intrinsic motives and, in turn, focusing on different behaviour-types (i.e. goal-directed and experiential). In fact, TAM-based studies are essentially work related and focused on utilitarian use (i.e. goal-directed use). Even though several papers (e.g. Agarwal and Karahanna, 2000; Davis et al., 1992; Igbaria et al., 1996) have introduced perceived enjoyment in Web -as intrinsic motivation-, they still focus on a task-oriented perspective. 4 Therefore, assuming by previous research that perceived enjoyment could occur during goaldirected activities, there may be differences between goal-directed and experiential users in the relative influence of the several determinants of Web usage. Activities can be perceived to be instrumental –i.e. extrinsicin achieving outcomes that are distinct from the activity itself. Likewise, activities can be performed for no apparent reinforcement other than the process of performing the activity –i.e. intrinsic-. Experiential and goal-directed users would not thus weight extrinsic and intrinsic motives in the same way when on the Web. For instance, as Hoffman et al. (2003) suggest, “the general and broad nature of flow measurement to date has precluded a precise investigation of flow during goal-directed versus experiential activities”. Furthermore, “one important future research area is specifying and testing conceptual frameworks which differentiate experiential and task-oriented flow. Conceptual models of flow which have been developed and tested to date do not in any way differentiate between experiential and task-oriented flow. The relative importance of antecedents of flow (…) may well differ across rational vs experiential processing modes”. Our objective is thus to evaluate the mediating role of main extrinsic and intrinsic motives explaining goal-directed (i.e. for work and to search for specific information) and experiential (i.e. traditionally associated with recreational surfing) acceptance and Web usage. The results could be used (1) to explain, and (2) to improve the users’ experience of being and returning to the Web. This paper is outlined as follows. First, the original version of the TAM is introduced. The next section starts with a brief outline of the framework and provides 10 hypotheses that can be derived from this framework. We then describe the research method which was adopted to validate the model. Results and analysis follow research design. Finally, we give an interpretation of the findings and discuss the contributions and limitations of our work. THEORETICAL BACKGROUND: A BRIEF PERSPECTIVE Research in the HCI (Human-Computer Interaction) tradition has long asserted that the research of human factors is a key to the successful design and implementation of technological devices, and should include extrinsic and intrinsic motives. In this context and following HCI-Research in the 5 MIS (Management Information System), individuals have a full range of opportunities to interact with technologies for different motives: extrinsic or intrinsic. Motives have been characterized as intrinsic, emphasizing internal rewards such as pleasure and satisfaction from performing the behaviour, or extrinsic, focusing on external rewards including, for instance, incentives and gratifications. It is thus important to consider different the motives based, respectively, on the TAM and the flow experience to understand the acceptance and Web usage. Technology Acceptance Model (TAM) Several researches have demonstrated the validity of TAM across a wide variety of IT, also including E-Mail and Web. Specifically and focusing our study on Web acceptance and usage, TAM suggests that there exists a direct and positive effect between attitude towards Web usage, usage intention and actual usage. Perceived usefulness and ease of use determine the attitudes toward using the Web. In turn, usage intentions are determined by these attitudes and perceived usefulness. Finally, usage intentions lead to actual Web use. :: Take in Figure 1 :: Perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989); as we commented above, the perception that users will want to perform an activity “because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself, such as improved job performance, pay, or promotions” (Davis et al., 1992). Perceived ease of use is defined as “the degree of which a person believes that using a particular system would be free of effort” (Davis 1989). On the one hand, perceived usefulness influences Web usage indirectly through attitude and directly through intent. On the other hand, as perceived ease of use has an inverse relationship with the perceived complexity of use of the technology, it affects perceived usefulness. TAM thus posits that perceived usefulness is influenced by perceived ease of use. A system that is difficult to use is less likely to be perceived as useful; in other words, between two systems offering identical functionality, a user should find the one that is easier to use more useful. Nevertheless, perceived usefulness is not hypothesized to have an impact on perceived ease of use. Davis 6 (1993) states that "(…) making a system easier to use, all else held constant, should make the system more useful. The converse does not hold, however”. Davis (1989) stated his original TAM model where he found a stronger support of perceived ease of use construct with perceived usefulness rather than with intention to use. “From a causal perspective, the regression results suggest that ease of use may be an antecedent to usefulness, rather than a parallel, direct determinant of usage”. Later, Davis (1993) noted that perceived ease of use may actually be a prime causal antecedent of perceived usefulness. These relationships have been examined and supported by many prior studies (e.g., Davis, 1989, 1993; Davis et al., 1989; Venkatesh and Davis, 1996, 2000). However, as we commented above, there is a significant body of theoretical and empirical evidence regarding the importance of the role of intrinsic motives in Web acceptance and use. Researchers have become increasingly aware of the relevance of the non-extrinsic motives of use such as intrinsically-enjoyable experiences (i.e., flow) in understanding attitudes and behaviours. Following, we evaluate the role of flow (1) affecting the Web-based behaviour as a highly-subjective variable among individuals, and, in turn, (2) explaining and improving the users’ experience of being in and returning to the Web. Flow Model Flow, defined as “the holistic sensation that people feel when they act with total involvement” (Csikszentmihalyi, 1975), has been recommended as a possible metric of the online user experience (Koufaris, 2002). Flow is a positive, highly-enjoyable state of consciousness that occurs when our perceived skills match the perceived challenges we are undertaking. When this occurs an individual derives intrinsic enjoyment from the activity and tends to continue with it. This is known as a state of flow. If the task is too easy the person becomes bored. If the work demands skills beyond the capabilities of the individual, anxiety is created. When our goals are clear, our abilities are up to the challenge and feedback is immediate. We become involved in the activity and intrinsically motivated. 7 A common measure of flow could be thus the level of perceived enjoyment of an activity, similar to the emotional response of pleasure from environmental psychology (see Koufaris, 2002). In fact, as Davis et al. (1989) stated, perceived enjoyment can be conceptualized as “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated”. Flow emphasizes a user's subjective enjoyment of the interaction with the technology, not a perception of the medium per se (Trevino and Webster, 1992). That is to say, the concept of flow is a possible metric of the online user experience, and could be defined as an intrinsically enjoyable experience. Many extensions to the original TAM have been proposed. Within the IS domain, Davis et al. (1992) applied motivational theory to understand new technology adoption and use. These authors proposed a new model, motivational model (MM). One factor was renamed (usefulness  extrinsic motivation) and one additional factor was introduced (perceived enjoyment as intrinsic motivation). As we noted above, extrinsic motivation describes an individual’s personal gain associated with the use of a particular technology. On the contrary, intrinsic motivation describes the perceived enjoyment associated to the use of a particular technology itself, different from possible performance outcome of the use (see also Vallerand, 1997, for a recent review). MM and TAM have conceptual and empirical similarities; in fact, usefulness and extrinsic motivation are quite similar. Venkatesh et al. (2002) introduced an extending TAM, which integrates the intrinsic motivation factor from the motivational model with the original TAM. The measures of intrinsic motivation included enjoyment with the system, pleasance of systems use, and fun of systems use. Most recently, Koufaris (2002) applied flow theory to online consumer behaviour to examine emotional and cognitive responses when visiting an online store. This author expected engagement with the site would result in intention to return to the store, outlined earlier as eloyalty. Results proved that product involvement, web skills, value-added search mechanisms, and challenges (to perform to best of user’s ability and ‘stretching’ user capabilities) led to shopping enjoyment, and ultimately to intention to return to the site. In this sense, intrinsically perceived enjoyment has been identified as an important intrinsicmotivational factor in Web acceptance and usage. For example, Davis et al. (1992) theorised that 8 perceived enjoyment directly influenced computer-usage intention (i.e. a word processing program, WriteOne). Also, Igbaria et al. (1996) studied the effect of perceived fun-enjoyment. In this study, support was found for a positive relationship between perceived fun-enjoyment and system usage among managers and professionals who either had a microcomputer on their desk or had ease access to one in the daily performance of their job. Perceived enjoyment associated by an individual with a particular act, could thus have a major impact on an individual’s affective response to the Web, its attitudes and behaviours. However, although regarding previous research perceived enjoyment could occur during goal-directed activities, experiential users are specifically moved by an intrinsic motive (e.g. "to feel pleasure and enjoyment from the activity itself"; Bloch et al., 1986), whereas among goal-directed users browsing appears to involve more extrinsic rewards than intrinsic rewards. There might be thus differences between goal-directed and experiential users in the relative influence of the various determinants (e.g. flow state) of Web acceptance and usage. Experiential users show ritualized orientations exploring the Web in their daily quest for the latest interesting sites. Users search for those opportunities which provoke them to further explore Web sites. Thus, experiential users do not essentially value the Web as a medium that lets them achieve set goals, but they browse orientated towards enjoyable navigational choices. It is an autotelic experience, where the experience itself acts as a primary intrinsic reward, even if extensive external rewards are present. On the contrary, when usage is extrinsic, instrumental issues such as perceived usefulness ought to come into one's main decision making criteria for future usage (adapted from Chin et al., 1996). Using the Web for its informational value and purchase utility -such as directly searching for information to complete a task or to reduce purchase uncertaintyare goal-directed behaviours, whereas relatively unstructured recreational use are experiential behaviours (see Hoffman and Novak, 1996). As Hoffman and Novak (1996) summarized, “goal-directed flow activities in a CME are instrumental and utilitarian in nature, extrinsically motivated, characterized by situational involvement, and result in directed search and learning. In contrast, experiential flow activities are 9 ritualistic and hedonic, intrinsically motivated, characterized by enduring involvement, and result in non directed search and learning”. See Table 1. :: Take in Table I :: Therefore, experiential and goal-directed users would not weight extrinsic and intrinsic motives in the same way when on the Web. Experiential users are involved in the activity for the affective responses it provides (desire for enjoyment plus exploration and playfulness) rather than for utilitarian purposes. We could thus find relevant difference in (1) the relation between flow and TAM-beliefs on the Web, and how (2) the flow impacts the attitude and intention to use Web. Specifically, intrinsic motives -such as perceived enjoymentshould influence on attitude towards usage and intention greater among experiential users than among goal-directed users. This positive subjective experience becomes an important reason for acceptance and performance an activity among experiential users even though they considerer the Web as relatively low in perceived usefulness. On the contrary, goal-directed users may be willing to tolerate (i.e. accept and use) an annoying interface in order to access to functionality (as a salient and expected reward) -that is the most important-, while flow will not be able to compensate for a system that doesn’t do a useful task. Likewise, according to self-perception theory (see Bem, 1972) and the over-justification effect (see Lepper et al. 1973), when people attribute their behaviour to external rewards, they discount interest as a cause of their behaviour, and intrinsic motivation will be, therefore, lower. It formalizes the idea emphasized in the psychology literature that the subject finds the task less attractive when offers an expected and salient reward for engaging in an otherwise enjoyable task. That is to say, the subject would then infer that behaviour is motivated by the reward itself rather than by intrinsically perceived-enjoyment. This effect will be stronger when external reward is a focus of central attention (i.e. goal-directed users) because the non-distraction increases the tendency for subjects to think about the reward. On the contrary, experiential users usually engage in unstructured recreational that reduces their tendency to think about a possible external reward. Based on the above comments, we propose the following hypothesis. See Figure 2. 16 them. As Csikszentmihalyi (1997) summarizes, “when a person is anxious or worried, for example, the step to flow often seems too far, and one retreats to a less challenging situation instead of trying a cope”. Otherwise, too much stimulation will lead experiential users to making errors and feel out of control (i.e., anxiety as a negative affective reaction toward Web use). The more confident and comfortable user feels on the Web, the more likely it is that he/she will enjoy it. Therefore, based on the above evidence, we propose the following hypothesis. See Figure 2. H9. Higher levels of perceived ease of use will be positively related to higher levels of flow (i.e., perceived enjoyment) H9.a: The relationship between ease of use and flow (i.e., perceived enjoyment) will be similar between experiential users and goal-directed users Because TAM is used as the baseline model, we also verify the following TAM hypothesized relationship in the context of Web. H10. Intention to use positively influences Web usage higher levels of intention to use will be positively related to higher levels of Web usage :: Take in Figure 2 :: METHOD A survey instrument was used to gather data to test the relationships shown in the research model. Data were collected from a sample of online questionnaires filled out by subscribers located in three discussion-mailing lists –administered by RedIrisabout different topics (e.g. experimental sciences, social sciences and humanities) in order to increase the diversity of respondents. On the one hand, our target users should declare using Web frequently to experiential (ranged from 5-7 on EXP1-item, and ranged from 1-3 on GOAL1-item see below) or goal-directed (ranged from 5-7 on GOAL1-item, and ranged 1-3 on EXP1-item) activities, adapting the descriptions proposed by Hoffman et al. (2003). The items were measured using a seven-point scale ranging 17 from “strongly disagree” to “strongly agree”. Respondents are thus clear as to the activity context within which they are responding. GOAL1. Goal-directed behaviour. I usually have a distinct or identifiable purpose for my browsing. EXPE1. Experiential behaviour. I usually surf or have no preconceived purpose for my Web experience. The exclusion of invalid questionnaires due to duplicate submissions or extensive empty data fields resulted in two final samples: (1) experiential users (221 individuals); plus (2) goal-directed users (119 individuals). Their main demographic-characteristics -age and sexare similar to an average Internet user (6th AIMC Internet User Survey, October-December, 2003). Sample demographics of the subjects are shown in Table II. :: Take in Table II :: On the other hand, in developing the survey instrument, we chose both single item and multiple item constructs. Single item questions had to be selected for some constructs (attitude and usage) because the survey was deemed to be too lengthy when every construct had multiple items. For the item constructs we adapted measures used in the reviewed literature (see Davis, 1989; Davis et al., 1989; Ghani and Deshpande, 1994; Novak et al. 2000; Olney et al., 1991; Raman and Leckenby, 1998; Van der Heijden, 2001). Specifically, according to perceived usefulness and ease of use scales, we adapted Davis ‘s (1989) scales. One additional item was introduced and adapted (“Browsing is interesting”, adapted from Van der Heijden, 2001) and one original item (“Browsing in my job would enable me to accomplish tasks more quickly“; adapted from Davis, 1989) was omitted because a previous analysis considered it included in other items related to productivity and efficiency. Adams et al. (1992) replicated the work of Davis (1989) to demonstrate the validity and reliability of his instrument and his measurement scales. They also extended it to different settings and, using two 18 different samples, they demonstrated the internal consistency and replication reliability of the two scales. On the other hand, Web users' flow experiences are multi-dimensional (Chen et al., 1999). Flow is a complicated construct. In our study, we have estimated flow by measuring enjoyment and concentration (see Ghani and Deshpande, 1994; Olney et al., 1991). The domain of content covered by the measures (i.e. enjoyment and concentration) is clearly specified and the measures constitute a relevant census of the content domain. As we commented above, perceived enjoyment is related to the psychological concept of “flow” (Csikszentmihalyi, 1975), which is described as an “intrinsically enjoyable experience”. In this sense, we operationalize intrinsic enjoyment as browsing enjoyment. Olney et al.’s (1991) four-item indices of hedonism were used to measure the enjoyment experienced while browsing. Also, according to Csikszentmihalyi and Csikszentmihalyi (1988), when one is in flow, “one simply does not have enough attention left to think about anything else”. User involvement is a key driver of user response and higher levels of involvement stimulate users to be more attentive to the information presented to them (see Andrews and Shimp, 1990; Petty et al., 1983). We measure it with a four-item scale adapted from Ghani and Deshpande (1994). However, two items (“I am deeply engrossed in activity”-“I am absorbed intensely in activity”) correlated highly (>0.90, p < 0.000) in both samples -once translated into Spanish-; the former was eliminated to avoid a redundancy. Flow was thus measured as a second-order construct, encompassing two first-order constructs: (1) enjoyment; and (2) concentration. The items for the dimension ‘flow’ were optimally weighted and combined using the PLS algorithm (PLS Version 3.00 Build 1058, Chin, 2003) to create latent variable scores. The resulting score more accurately form or precede the underlying construct than any of the individual items by accounting for the unique factors and error measurements that may also affect each item (adapted from Chin and Gopal, 1995). As a result, the dimensions or firstorder factors become the observed indicators of second-order factor. However, the presence of 19 multicollinearity was also checked and the low variation inflation factor (VIF < 10) indicated that multicollinearity of the research data was not of a concern. As Williams et al. (2003) note, “multidimensional constructs are often conceptualized as composites of their dimensions, such that the paths run from the dimensions to the construct. In such instances, the dimensions of the construct are analogous to formative indicators, (…) as opposed to the reflective indicators. Second, the indicators of a multidimensional construct are not manifest variables (…), but instead are specific latent variables that signify the dimensions of the construct. These latent variables require their own manifest variables as indicators, such that the manifest variables and the multidimensional construct are separated by latent variables that constitute the dimensions of the construct”. In our research, we have thus decided to propose a molar second-order factor. Flow is (1) viewed as a composite of enjoyment and concentration and (2) modelled as formative 1 . In this sense, “indicators could be viewed as causing rather than being caused by the latent variable measured by the indicators” (see MacCallum and Browne, 1993). In fact, the omission of a formative indicator may alter the construct itself. Formative indicators can thus touch upon different aspects of the composite variable. According to the Web-usage variable, it was operationalised by a self-reported measure of ‘the average time that an individual spends on a Web session’ adapted by a variable employed by Raman and Leckenby (1998) to measure Web interaction and Novak et al. (2000) to measure timeuse. As Gardner and Amoroso (2004) summarize, “though some research suggests that selfreported usage measures are biased (Straub et al., 1995), other research suggests that selfreported usage measures correlate well with actual usage measures (see Taylor and Todd, 1995a; Venkatesh and Davis, 2000)”. However, as the Web behaviour of our interest is neutral and not particularly sensitive (as data about income, ethnicity, financial practices, etc), self-reports tend to be accurate (adapted from Ajzen, 1988). 1 However, we tested two versions of the model -(1) with all constructs reflective and (2) with flow construct formativeand the results were qualitatively the same: no paths gained or lost statistical significance, and no significant paths changed in sign. Thus, the reader may be confident that the results are not an artifact of the author' modelling decisions. 20 The questionnaire is included in this paper’s Appendix I. We proposed finally 22 items corresponding to 7 constructs -plus a demographic section-. Our study was programmed to list the questions in a random order for each participant, avoiding potential systematic biases in the data. The scales were measured using a seven-point scale ranging from “strongly disagree” to “strongly agree” to unify scale types, excepting the usage construct in which indicator was measured ranging from “very little” to “very much”. Data Analysis A Structural Equation Modeling (SEM), specifically Partial Least Square (PLS), is proposed to assess the relationships between the constructs together with the predictive power of the research model. PLS was invented by Herman Wold, as an analytical alternative for situations where theory is weak and where the available manifest variables or measures would be likely not to conform to a rigorously-specified measurement model. In recent years, PLS procedure has been gaining interest and use among IS researchers (Aubert et al., 1994; Chin and Gopal, 1995; Compeau and Higgins, 1995; Roldán and Leal, 2003). We have used the Partial Least Squares (PLS) technique because this tool is primarily intended for predictive analysis in which the explored problems are complex, and theoretical knowledge is scarce. As stated by Wold (1985), "PLS comes to the fore in larger models, when the importance shifts from individual variables and parameters to packages of variables and aggregate parameters. (…) In large, complex models with latent variables PLS is virtually without competition". Furthermore, flow-construct is measured with formative indicators. PLS is appropriate for analyses of measurement models with both formative and reflective items. Being an emergent construct, they cannot be easily modelled using LISREL and other covariance-based approached since these approaches implicitly assume all indicators to be reflective (Diamantopoulos and Winklhofer, 2001). 21 Accordingly, Partial Least Squares via PLS-Graph 3.00 Build 1058 (Chin 2003) was used to analyse the data. The stability of the estimates was tested via a bootstrap re-sampling procedure (500 sub-samples). PLS model is analyzed and interpreted in two stages: (1) the assessment of the reliability and validity of the measurement model, and (2) the assessment of the structural model. This sequence ensures that the constructs’ measures are valid and reliable before attempting to draw conclusions regarding relationships among constructs (Barclay et al. 1995). RESULTS Measurement model For those constructs with reflective measures (i.e. latent constructs), one examines the loadings, which can be interpreted in the same manner as the loadings in a Principal Component Analysis. For constructs using formative measures (i.e. emergent constructs), the weights provide information as to what the makeup and relative importance are for each indicator in the creation/formation of the component. They are similar to when interpreting a canonical correlation analysis (Sambamurthy and Chin, 1994). Besides, it is necessary to bear in mind that no interdependencies among the formative items can be assumed, since the construct is viewed as an effect rather than a cause of the item responses. Therefore, indicators are not necessarily correlated and, consequently, traditional reliability and validity assessment have been argued as inappropriate and illogical for this type of high order factor (molar) with reference to its dimensions (Bollen, 1989). Thus, in our study, examinations of correlations or internal consistency are irrelevant for emergent constructs (flow-construct). Individual reflective item reliability is considered adequate when an item has a factor loading that is greater than 0.707 on its respective construct, which implies more shared variance between the construct and its measures (indicators) than error variance (Carmines and Zeller, 1979). All the reflective individual item loadings in our final models are above 0.707, excepting EASE6 (0.6891, 22 experiential-users model; 0.6680, goal-directed-users’ model). The results obtained are thus acceptable considering the exploratory nature of our study. See Tables III and IV below. Construct reliability analyses the internal consistency for a given block of indicators. This is assessed using the composite reliability (ρc) (Werts et al., 1974). We can use the guidelines offered by Nunnally (1978) who suggests 0.7 as a benchmark for a modest reliability applicable in initial stages of research. In our research, all of the latent constructs are reliable. They all have measures of internal consistency that exceed 0.7 (ρc). Also, we have checked the significance of the loadings with a re-sampling procedure (500 sub-samples) for obtaining t-statistic values. They all are significant. See Tables III and IV below. :: Take in Tables III and IV :: Average variance extracted (AVE) (Fornell and Larcker, 1981) assesses the amount of variance that a construct captures from its indicators relative to the amount due to measurement error. It is recommended that AVE should be greater than 0.50 meaning that 50% or more variance of the indicators should be accounted for. All latent variables of our models exceed this condition. See Tables III and IV above. Discriminant validity indicates the extent to which a given construct is different from other latent variables. To assess discriminant validity, AVE should be greater than the variance shared between the latent construct and other latent constructs in the model (i.e. the squared correlation between two constructs) (Barclay et al., 1995). All latent variables satisfy this condition. For this reason, we maintain the discriminant validity of the latent constructs of the models. See Tables V and VI below. :: Take in Tables V and VI :: Structural model 23 Tables VII to IX show the hypotheses, path coefficients (), t-values, and the variance explained (R2) in the dependent constructs. Figure 2 shows a graphical representation of the path coefficients () and the R2 values (variance accounted for) in the dependent variables, which allows a better understanding of the structural model. Consistent with Chin (1998), bootstrapping (500 resamples) was used to generate standard errors and t-statistics. Support for each general hypothesis on both samples can be determined by examining the sign and statistical significance of the t-values for its corresponding path. See Table VII and Figure 2. :: Take in Table VII :: :: Take in Figure 3 :: Both research models seem to have an appropriate predictive power for most of the dependent variables. The mean of the explained variance of the implied variables is 44.5% and 42.6% for experiential and goal-directed user groups respectively. See Table VIII. :: Take in Table VIII :: Moreover, hypotheses on intensity differences between both types of users (Hia) could be tested by statistically comparing corresponding path coefficients in these structural models. This statistical comparison was carried out using the procedure suggested by Chin (2000) to develop a multigroup analysis, which was implemented in Keil et al. (2000). According to this procedure, a t-test is calculated following the Equation 1, which follows a t-distribution with m+n-2 degrees of freedom, where Sp (Equation 2) is the pooled estimator for the variance, m and n are the sample of experiential and goal-directed users group respectively, and SE is the standard error of path in the structural model. Results are described in Table IX, presenting a wide support for the hypotheses put forward. :: Take in Equation 1 and 2: :: Take in Table VII :: Finally, since the study is a cross-sectional survey, it is problematic to draw causal inferences, and thus we avoid asserting causality in our comments. Also, according to the approach followed by 24 the Partial Least Squares technique, i.e. soft modeling, the concept of causation must be replaced by the concept of predictability (Falk and Miller, 1992). As can be seen from Tables VII and IX, the data supported the model and all hypotheses cannot be rejected on the basis of this empirical data. Intention. According to H10, intention is expected to have a positive relationship to usage; the relationship was found in both samples (experiential and goal-directed users). Attitude. H5 hypothesises a positive relationship between attitude and intention to use Web in both samples. The paths support the relationships hypothesised. This implies that attitude towards usage is a relevant mediator between perceptions and intention to use. Also, the relationship was significantly greater among experiential users than among goal-directed users, supporting H1a. Usefulness. In general, usefulness was expected to have a positive relationship to: attitude towards usage, H4, and intention to use, H3. On the one hand, the relationship usefulness –> attitude was found in both samples and, on the other hand, it was lesser among experiential users than among goal-directed users, thus supporting H4a. The relationship H3 (usefulness  intention to use Web), was not significant among experiential users, thereby partly rejecting H3. A possible explanation for this can be summed up in the following way: experiential users would not engage in an experiential and playful behaviour that also increases extrinsic rewards without previously adjusting their attitudes. Therefore, usefulness influences on intention to use Web among goaldirected users are greater than among experiential users, supporting H3a. Ease of Use. There were positive discernible relationships between ease of use  attitude (H6), ease of use  flow (H9) and ease of use  usefulness (H7), thus supporting the cited hypotheses in both samples. Specifically, the path coefficients (ease of use  attitude, H6; ease of use  usefulness, H7) were significant and statistically different between experiential users and goaldirected users; also, the relationships support the proposed intensities (H6a and H7a). The intensity of the relationship H9 (ease of use  flow) was similar between experiential users and among goal-directed users, supporting H9a 25 Flow. H1 hypothesises a positive relationship between flow and attitude towards usage in both samples. The path-coefficient supports the sign. Further, the hypothesised intensity (H1a) was found among goal-directed and experiential users. H2 hypothesises a positive relationship between flow and intention to use Web; the path-coefficients support the relationship hypothesised. H8 posits a positive relationship between flow and usefulness. The relationship was not significant among goal-directed users, thereby rejecting H8. Goal-directed users would be willing to tolerate an annoyed interface in order to access functionality that is very important, while no amount of flow will be able to increase perceived usefulness of a system that doesn’t do a useful task. Also, H2a and H8a posit greater influences among experiential users than among goal-directed users. Both relationships were significantly greater among experiential users than among goal-directed users, supporting H2a and H8a. R2. A number of findings -related to floware worth mentioning in particular (see Tables X and XI). The relative impact of flow on the behavioural intention can be examined by comparing the change in its R2 value when flow is removed from the model (see Table X). The effect size f2 can be calculated as ((R2full – R2excluded)÷(1 – R2full)). Cohen (1988) suggested 0.02, 0.15, and 0.35 as operational definitions of small, medium and large effect sizes respectively (see Chin, 1998). Excluding flow from the first model (based on experiential users) resulted in a drop of R2 to 0.398; in contrast, excluding flow from the second model (based on goal-directed users) resulted in a drop of R2 to 0.382. 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(2001), “Human-Computer Interaction Research in the MIS Discipline,” Communications of the Association for Information Systems, Vol. 9, pp. 334-355. 38 Figure 1. Technology Acceptance Model Usefulness Ease of use Attitude UsageIntention 39 Table I. Distinctions between goal-directed and experiential behaviour Goal-directed Experiential Extrinsic motivation Intrinsic motivation Instrumental orientation Ritualized orientation Situational involvement Enduring involvement Utilitarian benefits/value Hedonic benefits/value Directed (prepurchase) search Nondirected (ongoing) search; browsing Goal-directed choice Navigational choice Cognitive Affective Work Fun Planned purchases; repurchasing Compulsive shopping; Impulse buys Source: Hoffman et al. (2003) 40 Table II. Descriptive statistics of respondents' characteristics Users Our study* 6th AIMC Internet User Survey Experiential Goal-directed Age < 20 10.0 12.4 10.9 20-24 27.5 25.5 23.1 25-34 32.2 30.8 38.7 35-44 19.6 15.6 17.2 45-54 10.0 12.3 7.4 55-64 0.7 3.0 2.2 >64 0.0 0.4 0.4 N/A 0.0 0.0 0.2 Sex Males 68.0 65.1 71.6 Females 32.0 34.9 28.1 % estimated over samples of experiential and goal-directed users 41 Figure 1. Hypotheses Usefulness Ease of use Attitude UsageIntention Flow H4 and H4a H10 H5 and H5a H8 and H8a H7 and H7a H6 and H6a H9 and H9a H2 and H2a H3 and H3a H1 and H1a 48 Table VIII. Variance explained (R2) for experiential and goal-directed structural models Indicators Experiential users Goal-directed users Intention 0.428 0.410 Attitude 0.568 0.575 Usefulness 0.359 0.471 Flow 0.301 0.287 Ease of Use -.- -.- Web Usage 0.573 0.389 49 Equation 1. T-statistic with m+n–2 degrees of freedom nm Sp tdirectedGoalalExperienti 11     50 Equation 2. Pooled estimator for the variance 22 )2( )1( )2( )1( directedGoalalExperienti SE nm n SE nm m Sp         51 Table IX. T-tests for multi-group analysis H0 Standard errors (SE) Sp E - GD T-value Supported H0 Experiential Goaldirected F  A H1a E>GD 0.0825 0.0716 0.0789 0.1150 12.824*** Supported F  I H2a E>GD 0.0757 0.0743 0.0752 0.0420 4.911*** Supported U  I H3a GD>E 0.0710 0.0816 0.0749 -0.1190 -13.979*** Supported U  A H4a GD>E 0.0806 0.0867 0.0828 -0.2620 -27.836*** Supported A  I H5a E>GD 0.0325 0.1135 0.0720 0.0710 8.672*** Supported EOU  A H6a E>GD 0.1058 0.0624 0.0930 0.1950 18.445*** Supported EOU  U H7a GD>E 0.0811 0.0798 0.0806 -0.3170 -34.569*** Supported F  U H8a E>GD 0.0792 0.0953 0.0852 0.2470 25.507*** Supported EOU  F H9a E=GD 0.0581 0.0816 0.0672 0.0130 1.700ns Supported *** p < 0.001, ** p < 0.01, * p < 0.05, ns = not significant (based on t(338), two-tailed test) t(0.001; 338) = 3.319543035; t(0.01; 338) = 2.590452926; t(0.05; 338) = 1.967007242 52 Table X. Impact of independent variables on intention to use Independent variables Samples R2 full R2 excluded f2 F Attitude Experiential 0.428 0.340 0.1538** Significant Goal-directed 0.410 0.353 0.0966* Significant Usefulness Experiential 0.428 0.421 0.0122 ns Not significant Goal-directed 0.410 0.391 0.0322* Not significant Flow Experiential 0.428 0.398 0.0524* Significant Goal-directed 0.410 0.382 0.0475* Significant *Small: 0.02; **medium: 0.15; ***large effect: 0.35; ns: not significant 53 Table XI. Impact of independent variables on attitude towards use Independent variables Samples R2 full R2 excluded f2 F Ease of Use Experiential 0.568 0.495 0.1690** Significant Goal-directed 0.575 0.566 0.0212* Not significant Usefulness Experiential 0.568 0.512 0.1296* Significant Goal-directed 0.575 0.407 0.3953*** Significant Flow Experiential 0.568 0.524 0.1019* Significant Goal-directed 0.575 0.555 0.0471* Significant *Small: 0.02; **medium: 0.15; ***large effect: 0.35; ns: not significant 54 Appendix I. Scales* CONSTRUCT/Indicators INTENTION**  INTEN1 Given that I have access to the Web, I intend to use it  INTEN2 Given that I have access to the Web, I predict that I would use it ATTITUDE**  ATTIT1 I have a positive attitude towards using the Web USEFULNESS  UTILI1 Browsing improves my performance  UTILI2 Browsing increases my productivity  UTILI3 Browsing enhances my effectiveness  UTILI4 Browsing is interesting  UTILI5 Browsing is useful ENJOYMENT  ENJOY1 Browsing Web is pleasant  ENJOY2 Browsing Web is fun  ENJOY3 Browsing Web is entertaining  ENJOY4 Browsing Web is enjoyable CONCENTRATION  CONCEN1 When I browse, I am absorbed intensely in browsing  CONCEN2 When I browse, I concentrate fully on browsing  CONCEN3 When I browse, my attention is focused on browsing EASE OF USE  EASE1 Learning to browse is easy for me  EASE2 I find it easy to get Web to do what I want it to do  EASE3 My interaction with Web is clear and understandable  EASE4 I find Web to be flexible to interact with  EASE5 It is easy for me to become skillful at using Web  EASE6 I find easy to browse USAGE  USAGE1 On average, how much time would you estimate that you personally spend on each Web session? * Fulfilled in Spanish and then translated into English ** In our proposal ‘Browsing’ is employed as using-synonymous.