Page 51 - Exploring the Potential of Self-Monitoring Kidney Function After Transplantation - Céline van Lint
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 Patient Acceptance of a Self-Management Support System 49
variance regarding the acceptance and use of technology [29, 30]. These models are generic as they were aimed to apply across domains, and did not consider the different context of specific domains, such as eHealth or eCommerce. These generic theories and models have been used to formulate a renal transplant patient technology acceptance (RTPTA) model for a SMSS (Figure 1). In the remainder of this section, each determinant in the model is defined and provided with the theoretical justification.
Performance Expectancy
Performance expectancy (PE) is adapted from UTAUT [27] and is defined here as the degree to which renal patients believe that using the system will help them attain gains or make losses with the performance of their health management. It investigates if participants expect that the system can help them with monitoring their health. PE is strongly related to the perceived usefulness construct in TAM [31]. In many studies, PE has been shown to be one of the strongest predictors of behavioural intention [23, 24, 27] and it has been used in the health informatics domain before, for example by Ahadzadeh [23] and Beenkens [24]. This leads to the first hypothesis:
H1: Performance expectancy positively correlates with patients’ intention to use the SMSS.
Effort Expectancy
Effort expectancy (EE) is defined as the degree of ease associated with the use of the system [27], e.g., whether patients experience any difficulties using the system. Perceived ease of use (PEOU) in TAM is a theoretically similar construct and is mainly found an effective predictor for peoples’ use intention when they are new to a technology [27]. EE has been shown to have a significant effect on patients’ intention of using an e-health service [24]. This leads to the second hypothesis:
H2: Effort expectancy positively correlates with patients’ intention to use the SMSS.
Social Influence
Social influence (SI) is also adapted from UTAUT [27] and is defined here as the degree to which renal patients perceive that important others believe they should use the system. It refers to what people in the patients’ environment think of using the system. TRA, TPB, TAM2, and TAM3 refer to this construct as subjective norm [11, 26, 28, 32]. Venkatesh et al. were unable to find SI as an effective predictor for voluntary technology use [27]. However, they did find it to be an effective predictor in a compulsory use context, for example when the working environment requires using that specific software application; but only at a stage where people had limited use experience. In the context of health-management, patients’ usage of a technology is often voluntary, the decision on whether or not using a system might be influenced
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