As was shown earlier, several studies have incorporated perceived innovation characteristics as a variable influencing the adoption of computer-based innovations. There is a large body of research documenting the importance of relative advantage (or usefulness) to the adoption of computer-based innovations. For example, Karahanna et al.
(1999) found that perceived usefulness is the only belief underlying both attitude toward adoption and continuing use of computers. In a recent review of the literature on TAM studies, Legris et al. (2003) showed that, with very few exceptions, relative advantage has a significant positive relationship with attitude and behavioural intention.
Different approaches to the conceptualisation of relative advantage have been proposed.
Venkatesh and Morris (2000) suggested that there are three main categories of advantages:
hedonic, utilitarian and social. Eason (1988) proposed a framework for classifying the benefits of ICT for organisations. His framework implies that the organisation is viewed as a collection of resources deployed to handle a specific task. According to the framework, the benefits of ICT can be placed on a continuum from resource reduction to organisational enhancement. There are four major categories of benefits: saving of resources, improved productivity, improved support and organisational enhancement.
This framework has the potential to be used to examine the benefits of adopting computer-based innovations within a leisure context. Reduction of resources involves the accomplishment of the same outcome using fewer resources. Resources involved in using a computer technology include the saving of effort, money and time needed to complete the tasks.
Improved productivity refers to the optimisation of resources. Generally speaking, this type of benefit involves seeking to obtain more using the same resources, that is, to maximise the task outcomes in relation to the required inputs. Whereas the former two categories of benefits relate to the management of resources, and thus refer to more ‘quantitative’
aspects of using IT, improved support and personal enhancement are associated with more intangible or ‘qualitative’ benefits. Improved support refers to the seeking of new ways of achieving the personal objectives. Finally, personal enhancement benefits are related to achieving the more important and abstract personal objectives in life.
Several researchers have focused on the benefits associated with reduction of resources, notably saving of time (e.g. Davis et al., 1989; Al-Gahtani and King, 1999; Agarwal and Prasad, 1999; Oh et al., 2003) and effort (e.g. Oh et al., 2003). Bearing in mind that most of the research on the adoption of computer-based innovations has been developed within an organisational context, it is not surprising that improved productivity is one of the most featured benefits (e.g. Davis et al., 1989; Agarwal and Prasad, 1999; Al-Gahtani and King, 1999; Venkatesh, 2000; Anandarajan et al., 2002; Oh et al., 2003). Measures of improved support usually refer, for example, to improvements in the quality of the work (e.g.
Agarwal and Prasad, 1999; Al-Gahtani and King, 1999; Karahanna et al., 1999), improvements in accuracy of information (e.g. Moon and Kim, 2001) and enabling better decisions (e.g. Teo et al., 1999; Teo, 2001) and more imaginative work (Tsai et al., 2001).
As far as compatibility is concerned, the few studies that have incorporated this attribute have supported support its importance in explaining adoption and usage of computer-based innovations. For example, Al-Gahtani and King (1999) stated that the most striking aspect of the results of their study was the importance of compatibility. In a similar vein, Agarwal and Prasad (1999) found that compatibility was the most important predictor of usage.
The measures developed by Moore and Benbasat (1991) have been adopted by virtually all researchers incorporating this attribute in their research models (e.g. Agarwal and Prasad, 1999; Al-Gahtani and King, 1999; Oh et al., 2003). The scale includes three items, assessing
the extent to which the innovation (1) fits with work practices, (2) is compatible with all aspects of work and (3) fits with the way the individual likes to work.
As Legris et al (2003) demonstrated, the vast majority of studies on the adoption of technological innovations found a positive relationship between complexity (ease of use) and attitude towards, and intention to use computer-based innovations. The instrument developed by Davis et al. (1989) pervades the literature and has been used by several authors such as Agarwal and Prasad (1999), Al-Gahtani and King (1999), Karahanna et al.
(1999), Venkatesh (2000), Anandarajan et al. (2002) and Oh et al. (2003).
Other measures of ease of use include the mental effort required (e.g. Agarwal and Prasad, 1998; Al-Gahtani and King, 1999; Venkatesh, 2000; Moon and Kim, 2001), the time that takes to learn (e.g. Moon and Kim, 2001) and how hard it would be to learn without expert help (e.g. Moon and Kim, 2001). Some measures portrayed as measuring ease of use do not appear to do so. For example, several researchers (e.g. Al-Gahtani and King, 1999; Tan and Teo, 2000), following the recommendations of Moore and Benbasat (1991), included one statement regarding how frustrating is using the innovation. Answers to this type of statement may or may not reflect ease of use.
Moore and Benbasat (1991) defined visibility as the extent to which potential adopters see the innovation as being visible in the adoption context. Research suggests that visibility is a significant predictor of initial adoption (Karahanna et al., 1999) and usage (e.g. Agarwal and Prasad, 1997). Visibility of computer-based innovations has been operationalised in terms of ‘sight’ visibility, that is, whether the respondents see other individuals using the innovation (Karahanna et al., 1999; Oh et al., 2003; Karahanna et al., 1999).
In addition, the adoption of an innovation involves uncertainty about whether it will perform as anticipated. Thus, it is possible that an individual perceives risks associated with using computers and the Internet. For example, individuals may fear that their life might become much too dependent on these technologies or that it might result in a de-socialisation process.
As Moore and Benbasat (1991) and Venkatesh and Davis (2000) noted, individuals often respond to social normative influences to establish or maintain a favourable image within a reference group. There are contradictory results about the influence of image upon the adoption of computer-based innovations. Karahanna et al. (1999) found that image was a
significant predictor for users, Venkatesh and Davis (2000) concluded that image predicted perceived usefulness and Al-Gahtani and King (1999) that image predicted perceived n enjoyment. Conversely, Agarwal and Prasad (1997) found that image was not a predictor of usage and intention and Karahanna et al (1999) that image was not a predictor of attitude toward adopting.
4.3.2.3. Affect
Several researchers have assessed the individual’s emotional reactions towards using computer-based technologies. However, the range of feelings is rather limited to some negative feelings such as anxiety (e.g. Selwyn, 1997; Venkatesh, 2000; Bozionelos, 2001;
Tsai et al., 2001; Wilfong, 2006), the positive feeling of perceived fun/enjoyment (e.g.
Venkatesh, 2000; Teo, 2001; Anandarajan et al., 2002; Liaw, 2002) and the general feeling of liking (e.g. Agarwal and Prasad, 1999; Liaw, 2002; Yang and Lester, 2003). Results have shown that, in general, positive feelings tend to be associated with use whereas negative feelings deter people from using computing technologies.
Venkatesh (2000) defined computer anxiety as a negative affective reaction toward computer use and Bozionelos (2001) as a negative emotional state and/or negative cognition experienced by a person when he/she is using a computer or imagining future computer use. Computer anxiety has been shown to be related to a number of key variables including hours of computer use (Wilfong, 2006), experience with specific computer-related tasks (Wilfong, 2006), quality of initial experience (Todman and Drysdale, 2004;
Beckers and Schmidt, 2003), lack of support received during the first experiences (Beckers and Schmidt, 2003), quality of past experiences (Todman and Drysdale, 2004) and self-efficacy (Wilfong, 2006).
Other negative feelings include frustration and anger. According to Bessiere et al. (2006), frustration is almost universally accepted as the emotional outcome of a negative computing experience. They defined frustration as “an emotional response to unexpected obstacles impeding goal achievement” (p. 3). Frustration arises when the individual is faced with a condition that interferes with or stops the realisation of a goal. In other words, frustration occurs when the technology does not perform as the individual wants. Wilfong (2006)
suggested that anger, defined as a strong feeling of displeasure and negative cognitions in response to a perceived failure to perform a computer task, was a feeling that could be related to computer adoption and usage.
Perceived fun/enjoyment is the extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use (Venkatesh, 2000). Several researchers have also recognised the importance of perceived fun and include this belief in their conceptual frameworks (e.g.
Teo et al., 1999; Teo, 2001; Anandarajan et al., 2002; Liaw, 2002; Choi et al., 2003; Liaw and Huang, 2003). In some studies perceived enjoyment has been conceptualised as a component of another belief used in research on computer adoption – perceived playfulness. For example, Moon and Kim (2001) regard enjoyment as one of the three components of playfulness, together with how curious and attentive the individual is when using computers. In contrast, Venkatesh (2000) points out that playfulness refers only to how creative and venturesome the individual is and enjoyment is a separate construct.
Kay (1993) is one of the few investigators who has used a more comprehensive set of emotional responses. Adopting a multicomponent model of attitudes, Kay tapped the affective component by the means of 10 dimensions. Yet, the scale has been criticised by Noyes and Garland (2005) for three main reasons:
• It is debatable whether some of the descriptors are actually representing positive and negative aspects (e.g. natural/artificial);
• Some descriptions are difficult to apply (for example emptiness and suffocation);
• Certain dimensions can hardly be viewed as affective feelings states as postulated by Cohen and Areni (1991), such as goodness.