# Healthcare Students' Ethical Considerations of Care Robots in The Netherlands
## 3 Results
Therefore, we conducted a multiple regression analysis with an interaction term [35] to predict the Acceptance scores through interactions with Autonomy, Utility, and Maleficence. Age and Education Level served as control variables (i.e., moderators). Regarding assumptions, the Durbin-Watson of 2.032 was well within the range of 1.5-2.5, indicating the absence of autocorrelations. The variance-inflation factor (VIF) was acceptable (\(<\)5.0), suggesting no multicollinearity due to interdependency of variables ([PERSON], 2001). Three residual outliers based on a \(z\)-score \(>3.29\) or \(<-3.29\) were removed, which was 0.1% of the most extreme values. The residuals were mean centred. There were no linearity problems and the standardized predicted and residual plot showed no problems of heteroscedasticity. Overall, it was suitable to perform a regression analysis. The full model with five predictors (Autonomy, Utility, Maleficence, Age, and Education Level) explained 53.6% of the variance in Acceptance (\(R^{2}=0.54\), \(F_{(5,\,348)}=80.26\), \(p<0.001\)), also after Bonferroni correction (\(\alpha=0.05/5=0.01\)). The combination of the terms of interaction between Autonomy, Utility, and Maleficence with Age and Education Level as moderators added a marginally significant (according to Bonferroni correction) and small amount of explained variance (2%): \(\Delta R^{2}=0.02\), \(F_{(6,\,343)}=2.54\), \(p=0.020\). The total explained variance was 55.5% for this model (\(R^{2}=0.56\), \(F_{(11,\,342)}=38.83\), \(p<0.001\)). Furthermore, potential Maleficence had a significantly negative influence on Acceptance (\(\beta=-0.33\), \(t_{(342)}=8.05\), \(p<0.001\)). By contrast, increased Autonomy positively influenced Acceptance (\(\beta=0.18\), \(t_{(342)}=4.39\), \(p<0.001\)) just like Perceived Utility (\(\beta=0.41\), \(t_{(342)}=9.23\), \(p<0.001\)). Age also positively influenced Acceptance but not beyond the Bonferroni-corrected level (\(\beta=0.08\), \(t_{(342)}=1.99\), \(p=0.047\)), whereas Education Level was not significantly related to Acceptance (\(p>0.05\)). To test the relative weights of moral considerations and utility perceptions on the acceptance of healthcare robots, we performed a hierarchical regression (method Enter) with Autonomy and Maleficence (both indicating moral concerns) entered as predictors in the first block, and Utility in the second block, on the Acceptance measure as dependent variable. Autonomy and Maleficence together significantly explained Acceptance, \(R^{2}=0.48\), \(R^{2}{}_{\mathit{a}\mathit{i}\mathit{j}}=0.47\), \(F_{(2,409)}=184.75\), \(p=0.000\), which was mainly due to Maleficence. Utility added a significant increase of 9% in explained variance (\(R^{2}{}_{\mathit{change}}=0.09\), \(F_{(1,408)}=81.97\), \(p=0.000\)). The three predictors together explained a substantial amount of variance in Acceptance (\(R^{2}=0.56\), \(R^{2}{}_{\mathit{a}\mathit{i}\mathit{j}}=0.56\), \(F_{\mathit{change}(1,408)}=81.97\), \(p=0.000\)) in which the largest part is explained by the health professionals' moral considerations about care robots.