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Figure 5: Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003:447) In UTAUT, the three key constructs acknowledged to influence behavioural intention are performance expectancy, effort expectancy, and social influence while facilitating conditions and behavioural intention directly influences the use of a technology (Hennington & Janz, 2007). Simultaneously, these key constructs are subjective to moderating variables which includes: gender, age, experience and voluntariness of use (Venkatesh et al., 2011). In the context of this investigation, intention was defined as the degree to which an individual has formulated the consciousness to perform (or not to perform) a specific behaviour – ‘Use’ of a technology (Veer, Peeters, Brabers et al., 2015). The constructs of UTAUT are discussed in sections 3.5.2.1-3.5.2.4

3.5.2.1 Performance Expectancy

Performance expectancy is defined as the extent to which an individual believes that utilising a system is useful for their work, will help speed up work, accomplish their work, and

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improve productivity as well as enhance their decision making process (Venkatesh et al., 2003; Hennington & Janz, 2007). As elaborated in detail under the application of the UTAUT theory for this study in section 3.7, the essence of the performance expectancy construct, in particular, is that it brought forth the human perceptual aspects of adoption and the use of e-Health IS in the public healthcare sector. The root constructs of performance expectancy include perceived usefulness, extrinsic motivation, job-fit, relative advantage, and outcome expectations (Venkatesh et al., 2003).

Perceived usefulness originated from the TAM and C-TAM-TPB and is defined as the degree to which an individual determines that using a certain system would increase their efficiency and job performance (Davis, 1989). As elaborated in more detail under the application of the UTAUT in section 3.7, the belief that a technology innovation increases the efficiency and job performance of a user impacts on its performance expectancy. Extrinsic motivation originated from MM and is defined as the perception that an individual will perform an activity because it is presumed to be influential in realizing a valued outcome separate from the aforementioned activity (Hennington & Janz, 2007). A technology innovation that yields a rewarding and exceptional valued outcome such as reduction in process time influences the expectation of the user to accept and use the system.

Similarly, Job-fit, which originates from the MPCU is defined as the manner in which the capabilities of a system would enhance an individual’s job performance (ibid). In this regard, when a technology innovation is suitable for work processes, the performance expectation from the end-users tends to increase (Ifinedo, 2012). The aspect of job-fitness in the context of this study is elaborated in detail under section 3.7.

Another key construct under performance expectancy in UTAUT is that of relative advantage. It originates from IDT and is defined as the extent to which an innovation is assumed to be better than its antecedent. This assumption is based on the perception that a new technology innovation makes tasks/processes easier, faster and more productive than it used to be. It works with, and supplements perceptions on outcome expectations. In effect, outcome expectations are yet other constructs within the ‘Performance Expectancy’ concept as derived from SCT. Outcome expectancy is further classified into performance and personal outcomes. Personal outcomes deal with a sense of individual accomplishments such as enhanced productivity and improved level of professionalism from system usage.

The relevance in this study is that performance outcomes address job-related outcomes such as promotions and incentives, which are a fraction of success or failure in the use of task enhancing tools and systems in fields such as healthcare service delivery (this is elaborated in more detail under the application of the ATAUT theory in section 3.7). The moderating factors that have effects on the relationship between performance expectancy

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and behavioural intention are gender and age (Venkatesh et al., 2003). The application of the root constructs of performance expectancy and the moderating factors in this study are discussed in section 3.7.1.

3.5.2.2 Effort Expectancy

Effort expectancy is defined as the magnitude of ease associated with the utilization of a system (Venkatesh et al., 2003). The effort expectancy construct explains the views on the exertion level that accompany the use of e-Health IS in the public healthcare institutions and as such, elaborated in detail in section 3.7.

The root constructs of effort expectancy is perceived ease of use, ease of use and complexity (Hennington & Janz, 2007). Perceived ease of use originates from TAM, and defined as the extent to which an individual perceives that using a certain system would be effortless (Davis, 1989). This construct implies a relatively low difficulty level in the usage of a system as perceived by the user while operating the system on a daily basis and easily finding the system easy to use. Similarly, simplicity or ease of use originates from IDT and defined as the extent to which a technology innovation is assumed as easy to use. This can be likened to the perceived ease of use originating from TAM.

Moreover, complexity originates from the Model of PC Utilization. It is defined as the degree to which a technology innovation is perceived as relatively challenging to understand and use. Thus, complexity points to the perception of the users about the stress level in comprehending how to use a system. The root constructs of ‘Effort Expectancy’ is elaborated in more detail under the application of the ATAUT theory in section 3.7.

The moderating factors that have effects on the relationship between effort expectancy and behavioural intention relationship are gender, age, and experience in the UTAUT model (Venkatesh et al., 2003). The application of the root constructs of effort expectancy and its moderating factors are discussed in section 3.7.2.

3.5.2.3 Social Influence

Social influence is defined as the extent to which an individual perceives that important others believe he or she should use a new technology innovation (Venkatesh et al., 2003).

The social influence determinant and its root constructs reflect the notions of an individual’s behavioural intention as influenced by their perceptions of the manner in which other people label him/her as a user of e-Health IS in the public hospitals. The application of social influence to this study is further broken down in section 3.7.

The basis of social influence includes subjective norm, social factors and image (Venkatesh et al., 2003). Subjective norm originated from TRA, TAM, TPB and C-TAM-TPB and is

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defined as an individual’s perception that significant people expect him/her to or not to perform a particular behaviour (Venkatesh & Davis, 2000). It discloses that important others positively influence the user to actually use a system for their daily activities. Subsequently, it aims at presenting the influence of social factors on the users of the system in a public hospital environment.

Social factors, as key root constructs of ‘Social Influence’, were drawn from the MPCU. It is defined as an individual’s internalization of a group’s subjective culture and interpersonal agreements shaped in a particular social situation. It discloses the importance of peers and the organization management in assisting and supporting a user in actually using a system for their daily work activities.

In addition to social factors, image is another root construct that originates from IDT and is defined as the extent to which the use of a technology innovation is assumed to enhance an individual’s persona or status in his/her social environment (Hennington & Janz, 2007).

Furthermore, image connotes that users feel a higher sense of prestige from using the system than other individuals who do not use the system in the public hospital context. This is elaborated in more detail under the application of the UTAUT in this study in section 3.7.

The moderating factors that have effects on the relationship between social influence and behavioural intention are gender, age, experience and voluntariness in the UTAUT model (Venkatesh et al., 2003). The application of the root constructs of social influence and its moderating factors were discussed in section 3.7.3.

3.5.2.4 Facilitating Conditions

Facilitating conditions are defined as the organizational plan and technical infrastructure that exists to directly support an individual to use of a technology innovation (Venkatesh et al., 2003). The facilitating condition construct discloses the measures put in place by an organization including technical and personnel aspects to influence the use of e-Health IS in the public hospitals. This was further discussed in the application of UTAUT in this study, in section 3.7.

The basis of this key construct includes perceived behavioural control, facilitating conditions, and compatibility (Hennington & Janz, 2007). Perceived behavioural control is adapted from the TRA, TPB and C-TAM-TPB. It “reflects perceptions of internal and external constraints on behaviour and encompasses self-efficacy, resource facilitating conditions, and technology facilitating conditions” (Hennington & Janz, 2007: pp 63). Facilitating conditions originate from the MPCU. They are “objective factors in the environment that observers agree make an act easy to do, including the provision of computer support” (ibid). Compatibility originated

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from IDT and is defined as the extent to which a technology innovation is perceived as consistent with needs, existing values and experiences for potential users.

This root constructs of facilitating conditions point out the provision of resources to be put in place by the organization such as software, hardware, IT personnel support and well-suited systems, to serve as catalysts in acceptance and use of a technology innovation. This is further elaborated in the application of UTAUT in this study in section 3.7.

The moderating factors that have effects on the relationship between facilitating conditions and use behaviour are age and experience in the UTAUT model (Hennington & Janz, 2007).

The application of the root constructs of facilitating conditions and its moderating factors in this study are discussed in section 3.7.4.