Page 52 - Exploring the Potential of Self-Monitoring Kidney Function After Transplantation - Céline van Lint
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 50 Chapter 3
by health-providers, family members, or fellow patients. Kim and Park have reported subjective norm to have a strong indirect association with patients’ behavioural intention of using health information technology via perceived usefulness [25]. This leads to the third hypothesis:
H3: Social influence positively correlates with patients’ intention to use the SMSS.
Facilitating Conditions
The factor referred to as facilitating conditions (FC) is often put forward as an effective predictor [27, 33]. In the current model, FC is defined as the degree to which renal patients believe that there are objective factors available in their environment to support their use of the system [27]. Examples of these objective factors include a computer that is appropriate for use of the system, and the availability of supporting others who can help to use the system if needed. Studies have reported mixed outcomes concerning the relevance of facilitating conditions for behavioural intention[27, 34, 35]. In the eHealth domain, however, facilitating conditions are considered an important predictor of patients’ acceptance [22]. This leads to the fourth hypothesis:
H4: Facilitating conditions positively correlate with patients’ intention to use the SMSS.
Affect
Affect (AF) is defined as the renal patients’ overall affective reaction towards using the system. It addresses whether individuals find it pleasant to use the system. TRA, TBP, TAM nor UTAUT include the emotional reaction in performing the intended behaviour directly in their model. Instead, emotional outcomes are only indirectly included in the models as attitude towards the intended behaviour [12, 32, 36, 37]. Others have argued for the inclusion of affect as a separate construct because one’s liking of a technology could influence his or her actual usage of this technology [38]. For example, computer games are used in healthcare domain because they have the advantage of entertaining people in otherwise painful or boring health promoting processes [39]. Anxiety, as the opposite of liking, is expected to negatively influence system use [38]. In fact, affect has been found to be a predicting factor for general IT usage [38]. This leads to the fifth hypothesis:
H5: Affect positively correlates with patients’ intention to use the SMSS.
Self-efficacy
Self-efficacy (SE) is a key factor in predicting people’s behaviour as it determines if they will initiate certain behaviour, how much effort they will spend on it, and how they will cope with potential obstacles [40]. In the current model, SE is defined as the degree to which renal patients judge themselves capable of using the system to manage their health, which is in line with Compeau and Higgins[38]. The concerning items
























































































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