Page 46 - IJSA, Vol. 6, No 2, 2023

International Journal of Science Annals, Vol. 6, No. 2, 2023
рrint ISSN: 2617-2682; online ISSN: 2707-3637; DOI:10.26697/ijsa

Table 3
Regression Coefficients*
Unstandardized Standardized Collinearity statistics
coefficients coefficients
Model Std. t Sig.
B Beta Tolerance VIF
Error
(Constant) 0.359 0.062 - 5.790 0.000 - -
TechChar 0.429 0.218 0.139 1.967 0.042 0.257 3.889
CloudComRead 0.407 0.148 0.256 2.756 0.023 0.370 2.701
OrgAsp 0.759 0.214 0.284 3.549 0.016 0.234 4.272
EnvtAsp -0.136 0.205 -0.123 -.664 0.509 0.228 4.387
IndChar 0.595 0.195 0.182 3.051 0.017 0.263 3.803
RiskAnCtrl 0.487 0.182 0.413 2.681 0.009 0.331 3.018
SocAsp -0.701 0.193 -0.600 -3.632 0.001 0.289 3.458
Note. *Dependent variable – cloud-based Hospital Management System.
Results demonstrated in Table 3 indicate that with the Table 3 also measured the existence of multi collinearity
exception of environmental aspects, the rest of the by using the Variance Inflation Factor (VIF). The rule of
constructs showed a significant contribution to the thumb indicates that for multicollinearity collinearity to
successful implementation of a cloud-based HMS. exist, the value of VIF>10. However, as indicated in
Social aspects had the highest contribution with the Table 3, all the VIF values were less than 5, which
prediction power of 60.0% (β=0.600) at p=0.001; indicated that multicollinearity does not exist.
followed by risk analysis and control with a prediction Based on the findings, the conceptualized model for
power of 41.3% (β=0.413) at p=0.009. On the other cloud-based Hospital Management Systems for the
hand, environmental aspects’ contribution of 12.3% at South African public health sector was derived as
p=0.509 was relatively high, it was found not to be demonstrated in Figure 3.
significant.
Figure 3
Model for Cloud-Based Hospital Management System

*Note. TOE – Technology-Organization-Environment framework.

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