Using the Technology Acceptance Model to assess the impact of decision difficulty on website revisit intentions Introduction Technology enables consumers to undertake everything from relatively simple functional tasks (e.g., online banking transactions) to complex leisure activities (e.g., Massive multiplayer online role-playing games MMORPG). The Technology Acceptance Model (TAM), designed to determine the likely level of acceptance of a technological system in the workplace environment (Davis et al., 1989), has now been applied extensively in the consumer context (e.g., Childers et al., 2001). Various studies show that TAM successfully applies both at the level of the technologies themselves and in relation to individual technological channels such as websites. All these applications are outside the original scope of the TAM conceptualization, and this has lead to ad-hoc modifications to TAM that make it more suitable to each particular study s context. This has led to several possible TAM configurations in consumer research. While the general principles of TAM have been considered in the consumer context, and various individual antecedents have been added in specific studies, the impact of the difficulty of the task the consumer is undertaking on technology acceptance has not been examined. Task difficulty is the subjective evaluation of the complexity of an activity. Many of the decisions that consumers use technology for are inherently complex; for instance, an online search for information about a product involves the consumer (i) using multiple routes to obtain information, (ii) considering multiple outcomes, not all of which would necessarily be acceptable, and (iii) evaluating different outcomes which have conflicting interdependencies among the options available (e.g., price/ quality/ availability). Clearly even relatively common consumer online activities can be quite complex tasks (Campbell, 1988). While there is some TAM research considering technological complexity in a work-related context (Teo, 2010), there is little research that has looks at complexity/difficulty and technology adoption in the consumer context. The aims of this paper are to compare the different TAM configurations suggested for the consumer context and to consider how task difficulty impacts on consumers behavioural intentions in technology adoption. The literature provides a brief overview of TAM and discusses how the model has been modified for use in the consumer context. Four possible TAM configurations in the consumer context are identified. The literature then considers task difficulty and its relationship to the Technology Acceptance Model. The analysis examines the four TAM configurations to investigate the impact of task difficult on consumers likelihood to adopt a website, before discussing the results. The Technology Acceptance Model (TAM) The Technology Acceptance Model (Davis et al., 1989) provides an explanation of computer system adoption that is applicable to a broad range of workplace technologies and users. TAM is based on the Theory of Reasoned Action (Fishbein & Azjen 1975) and states that behavioural intentions are driven by the user s attitude towards the technological system in question and his/her perception of its usefulness. Attitude is driven by perceived usefulness and perceived ease of use. Perceived usefulness (PU) is defined as the prospective user s subjective probability that using a specific application system will increase his or her job performance, while perceived ease of use (PEOU) is the degree to which the prospective user expects the target system to be free of effort (Davis et al., 1989, 985). TAM applications in the consumer context consider technology adoption concerned with the use of shop-bots
online (Gentry & Calantone, 2002), online auctions (Stern et al., 2008) and website usage (Childers et al., 2001), communications technology in the form of email and instant messaging (Stradler et al., 2007), as well as the adoption of hardware such as handheld internet devices (Brunner & Kumar, 2005). TAM has also been successfully applied across a variety of consumer groups and works well in a cross-cultural environment (McCloskey, 2006; Singh et al., 2006). While these studies support the premise that TAM applies broadly, many applications of the model in the consumer context include modifications to the core TAM. Adaptations that have been made to TAM in the consumer context include adding additional components an affective or hedonic component alongside PU and PEOU that helps account for the voluntary nature of consumers adoption of technology (Childers et al., 2001). This fun or enjoyment factor is not an antecedent of TAM, but is instead an integral part of the model (Lee & Chang, 2011). The addition of another construct to the TAM is not the only change apparent in the consumer research area. There are also differences in how PEOU acts upon later aspects of the model, and whether PU, PEOU and the enjoyment element have a direct relationship with usage/intentions, or whether their impact is indirect. In the original TAM, PEOU has a direct impact on attitude as well as an indirect impact through PU. However, when an enjoyment element is added alongside PU, a question arises concerning whether a direct relationship between PEOU and attitude occurs or whether its influence on attitude is through PU and enjoyment (Brunner & Kumar, 2005; Koufaris, 2002; Stern et al., 2008). This is indicated in Figure 1 by the relationship labelled 1. In addition, the original TAM shows a direct relationship between PU and behavioural intentions. However, this is not always found with TAM applications in the consumer context (Brunner & Kumar, 2005, Dabholkar & Bagozzi, 2002; McKechnie et al., 2006; Singh et al., 2006). The lack of a direct relationship between perceived usefulness, and enjoyment, to behavioural intentions is indicated by the relationships labelled 2 in Figure 1. The presence/absence of these relationships leads to four possible TAM configurations in the consumer context: (A) with relationships 1 and 2; (B) without relationship 1, but with relationships 2; (C) with 1, but without 2; and, (D) without any of the relationships. Complexity/Difficulty Consumers can choose when, and to what extent, they adopt technological solutions to their problems. While many different antecedents of TAM have been considered, the impact of task difficulty on technology acceptance has not been isolated in the consumer context. Perceived technological complexity has been shown to impact on attitudes towards technology use directly, and through PEOU, in relation to employees (Teo, 2010). However, the difficulty of the task being undertaken does not seem to have been investigated. Yet task difficulty that is, the actor s perception of the complexity of the task (Campbell, 1988) is known to impact on consumer evaluations. For instance, task difficulty impacts on the consumer s assessment of their own ability to achieve a satisfactory solution to a problem (Kruger, 1999) and can influence product choice (Burson, 2007). While the relationship between task difficulty and TAM has not previously been explored, Teo s (2010) findings concerning technological complexity may extend to task difficulty. A question remains, however, as to whether the inclusion of enjoyment in TAM will mediate the direct relationship found between complexity and attitude found by Teo (2010). This question arises as a difficult task can be more desirable because it is more interesting than a simple task (Tsang et al., 1996). As such, increasing task difficulty may increase enjoyment, which may in turn lead to more positive attitudes and greater likelihood to adopt a technology. Yet as not everyone reacts positively to enriched tasks (Kim, 1980), increasing task difficulty may
lead to lower levels of enjoyment. The impact of task difficulty on PU also needs to be explored. The addition of task difficulty to this study leads to the conceptual model(s) represented in Figure 1. Figure 1: Consumer Technology Acceptance Model Research Design Procedure. One hundred and ninety three respondents from a large European city completed an online task alongside a questionnaire. The average age of respondents was 31 years, with equal proportions of men and women. All respondents were screened to ensure they were familiar with the online environment: they spent, on average, 12.2 hours per week connected to Internet. The stimuli for the task used existing websites. Respondents then completed a questionnaire measuring their attitude toward the website, perceived ease of use of the website, its perceived usefulness, their level of enjoyment associated with the website and the likelihood that they would use the website again. They also reported their perception of the difficulty of the task. Stimuli. The task consisted of scheduling flights based on arrival and departure constraints for a journey incorporating three European cities. This required four journeys. Each journey represented a separate task element constrained by two information cues (i.e., depart after, arrive before). For each journey multiple flight options were presented that would fulfil the constraints. An additional distinct, informational element to the activity was the inclusion of a museum visit in one of the cities journeyed to. Measures. Respondents completed a scale to assess task difficulty by focussing on the flight booking aspect of the tasks (How difficult was the task as a whole? How difficult was it to find the flight times? How difficult was it to choose appropriate flight times?), with similar items concerned with the museum aspect of the task. Respondents were instructed as follows: Thinking about the task you have just completed, please answer the following questions. This scale ranged from one (not at all difficult) to seven (extremely difficult). Three TAM elements perceived ease of use, perceived usefulness and enjoyment attitude towards the website, and re-visit intentions were assessed after the task was completed. The TAM items were based on Childers et al. (2001) and measured Perceived Usefulness, the degree to which the system or technology will improve user performance/ outcomes, with four items; Perceived Ease of Use, the process involved in using the system or technology, was measured using four items; and Enjoyment, the extent to which the activity of using the technology is
perceived to provide reinforcement in its own right, was measured using eight items. All of the TAM dimensions were measured using 7-point Likert scales. Attitude towards the website was measured using a 7-point semantic differential scale with three adjective pairs (good/bad, favourable/unfavourable, like/dislike) (Coyle & Thorson, 2001). Finally, for behavioural intentions respondents were asked to indicate, using a 7-point semantic different scale, how likely they were to revisit the website they had just used. Three adjective pairs (unlikely/likely, improbable/probable, uncertain/certain) taken from Brunner and Hensel (1996) were used for this variable. Analysis The four TAM configurations, including the task difficulty construct, share the same measurement model. CFA indicated this model had a poor fit: CMIN (335) = 818.974, p =.000, CMIN/DF = 2.445, GFI =.770, TLI =.879, CFI =.893, RMSEA =.087. Examination of the factor loadings and modification indices revealed several problematic items. These were removed sequentially and the resulting measurement models reassessed. In total six items were removed. Three from the TAM enjoyment construct (including the two reversed coded items), two from the task difficulty assessment, and one from the TAM usefulness construct. The modified measurement model had an acceptable fit: CMIN (192) = 285.629, p =.000, CMIN/DF = 1.488, GFI =.885, TLI =.972, CFI =.977, RMSEA =.050. The remaining assessment of the measurement properties of the scales were based on the modified model. The AVEs of all constructs were acceptable (>.5) and discriminant validity was demonstrated (Table 1). Reliability was clearly demonstrated for most of the scales (CR >.7). However, the CR was low for the task difficulty construct (.57). Nevertheless, while this value was below the ideal level, removing any of the four items on the scale would result in some of the elements present in the task being removed from the assessment of task difficulty. Four structural models were compared to take into account the different configurations of TAM in the consumer context. All four models had acceptable fit (Table 2). The model comparisons indicated significant differences between the four models. These difference were clearest (p=.000) between the two models that included the direct paths between usefulness/ enjoyment and revisit intention (paths 2 in Figure 1) and the two without them. That is, the models without paths labelled 2 (C/D) had a poorer fit that those with these paths (A/B). Models A and B were also significantly different ( χ 2 (1) = 4.364, p =.037). Model fit statistics indicated that model A Table 2: Structural Model Fit Statistics Model χ 2 DF p χ 2 /DF GFI TLI CFI RMSEA A(1,2)* 300.029 196.000 1.531.880.970.974.053 B(2)* 304.394 197.000 1.545.877.969.973.053 C(1)* 328.895 198.000 1.661.871.962.968.059 D 333.417 199.000 1.675.868.961.967.059 *(1,2) indicate optional paths included in the model Table 1: Construct Reliability and Validity CR 1 2 3 4 5 6 Task difficulty 1.57.518 TAM ease of use 2.83.285.762 TAM usefulness 3.89.276.674.861 TAM enjoyment 4.79.058.384.383.642 Attitude 5.89.255.516.588.338.816 Revisit intention 6.89.100.371.396.391.424.864 Bold values are AVEs, values beneath the diagonal are squared correlations. had a slightly better fit than model B, indicating that a direct relationship between ease of use and attitude needed to be included in TAM. Figure 2 shows the path coefficients for Model A. The regression weights confirmed the direct relationship between ease of use and attitude previously indicated by the model fit statistics. However, this relationship was relatively weak (.231) and not highly significant (p<.05). The model also confirmed the need for direct relationships between both usefulness and enjoyment, and
revisit intentions. Nevertheless, the relationship between usefulness and revisit intention was weak (.190) and not highly significant (p<.05). Task difficulty acted on technology adoption through each of use; the relationship here was strong (-.537) and highly significant (p<.001). Overall, task difficulty had a total effect on revisit intention of -.312. The impact of ease of use itself was strong but indirect (.581), with most of this (.506) being through the other TAM dimensions of usefulness and enjoyment. Usefulness had both a direct effect (.190) and an indirect effect through attitude (.162), while enjoyment had stronger direct effect than usefulness (.330), but no indirect effect. Figure 2: Model A - Standardised Regression Weights Discussion/Conclusions The original aims of this study were to (i) compare the different TAM configurations in the consumer context, and (ii) consider how task difficulty impacts on behavioural intentions. First, examining the TAM configurations clearly showed that the direct relationships between usefulness and enjoyment, and revisit intention, were required when assessing consumer technology adoption (i.e., paths 2 in Figure 1). This was particularly the case with enjoyment as it had no impact on behavioural intention through attitude. This finding extends those of Childers et al. (2001) as they did not include behavioural intention in their study. The impact of ease of use on both usefulness and enjoyment was strong (>.7) while the relationship between ease of use and attitude (i.e., path 1 in Figure 2) was relatively weak (>.25). However, model comparisons indicated that the relationship between ease of use and attitude was needed. The similarity of the fit statistics for the models with and without this relationship indicates that the relationship s inclusion in a general consumer TAM requires further investigation. Second, task difficulty clearly impacted on consumers intentions to revisit the website. This impact was observed through ease of use. That is, the more difficult the task is perceived to be by consumers, the more critical it is that managers ensure that the technology (or, in this case, specific website) used to execute the task is ease to use. It is interesting to note that the design of the airline website used in this task has now been changed so that planning the type of journey described in this study is much more straightforward than previously was the case. That is, the ease of use of the website has been improved for complex tasks. Task difficulty had no impact on either usefulness or enjoyment. There was no indication from the literature that a direct impact between difficulty and usefulness would be present, however, previous research did suggest that a direct relationship between task difficulty and enjoyment might exist. It may be that the relationship between task difficulty and enjoyment is moderated by individual characteristics such as need for cognition (Cacioppo & Petty, 1982) and this could indicate an avenue for future research.
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