Page 61 - Exploring the Potential of Self-Monitoring Kidney Function After Transplantation - Céline van Lint

Table 2. Construct reliability Constructs
Performance expectancy
Insight (PE1, PE2, PE3)
Health improvement (PE4, PE5, PE6) Time (PE7, PE8)
Effort expectancy Facilitating conditions Affect
Self-efficacy
Trust
Behavioural intention
Correlations
Cronbach’s 
.56 .73 .15 .93 .67 .99 .75 .21 .77 .79
Items to delete
PE6 - EE3
SE3, SE4
Cronbach’s  if items deleted
.54 - .73
.85
Patient Acceptance of a Self-Management Support System 59
shows correlations between the factors of RTPTA model at T1. Performance expectancy (both insight and time dimension), affect, and trust correlated significantly with behavioural intention. These factors also correlated with each other. shows the results of controlled correlations between behavioural intention and the four (sub-)factors when controlled for the other (sub-)factors that correlated with behavioural intention. Only affect had a significant correlation with behavioural intention when controlled for other (sub-)factors.
Regression Analysis
Hierarchical multiple linear regression was conducted on behavioural intention. Bootstrapping with 1000 samples was again applied. First, affect, the factor that partially correlated with behavioural intention, was entered as a predictor (model 1). After this, all remaining factors that correlated with behavioural intention were entered into the model (model 2). Model 1 resulted in a significant (F(1, 44) = 15.80, p < .001 ) model with R2 of 0.26, meaning that affect could account for 26% of the variance between patients’ usage intention, and the p-value suggests it was a significant predictor (table 6). Although Model 2 has its R2 improved (0.38), it was not found significantly better in explaining behavioural intention (R2 change = 0.12,
3