Table 1
Principal Components Analysis of five Likert items indicating a managerial role orientation (N = 63).
| Items | Factor loadings | |
|---|---|---|
| (1) | The quality of judicial decisions suffers severely from court budgeting54 | 0.70 |
| (2) | Judges insufficiently take into account whether the costs of their efforts outweigh the benefits | 0.65 |
| (3) | Judges ought to give better account of the time spent on handling cases | 0.79 |
| (4) | It is undesirable that the budget of courts is dependent on the number of cases handled55 | 0.32 |
| (5) | Team leaders ought to conduct themselves more as managers | 0.38 |
| Eigenvalue | 1.8 | |
| Variance explained (%) | 35.5 | |
| Cronbach’s alpha | 0.51 | |
Table 2
Principal Components Analysis of eight Likert items indicating a rule of law role orientation (N = 49).
| Items | Factor loadings | |
|---|---|---|
| (1) | The selection procedure for judges needs to be stricter | 0.46 |
| (2) | Judgments contain too much jargon56 | 0.40 |
| (3) | Politicians interfere too much with individual court cases | 0.70 |
| (4) | Citizens lack the required knowledge to assess the correct value of judicial decisions | 0.62 |
| (5) | The council of the judiciary poses a threat to the independence of the judge | 0.41 |
| (6) | Adjudication by laymen is undesirable, because it undermines the quality of the judiciary | 0.58 |
| (7) | The use of algorithms in judicial decision-making jeopardises the quality of the judiciary | 0.68 |
| (8) | The approval of having ancillary positions in addition to being a judge ought to be restricted further | 0.52 |
| Eigenvalue | 2.4 | |
| Variance explained (%) | 29.7 | |
| Cronbach’s alpha | 0.64 | |
Table 3
Principal Components Analysis of ten Likert items indicating the risk-benefit perception of involving judicial assistants in judicial decision-making (N = 75).
| Items | Factor loadings | |
|---|---|---|
| The contribution of judicial assistants to judicial decision-making | ||
| (1) | …provides an invaluable assistance to the judge | 0.68 |
| (2) | …harms the reputation of the judiciary57 | 0.65 |
| (3) | …enables the judge to focus on his core tasks | 0.40 |
| (4) | …dilutes the ultimate responsibility of the judge for decision-making58 | 0.47 |
| (5) | …relieves the judge | 0.34 |
| (6) | …influences the outcome of the case in improper ways59 | 0.61 |
| (7) | …diminishes the authority of the judge60 | 0.74 |
| (8) | …keeps the judge on edge | 0.43 |
| (9) | …causes the judge to pay less attention to the specific circumstances of a particular court case61 | 0.35 |
| (10) | …burdens the judge with extra work in terms of controlling and managing judicial assistants62 | 0.56 |
| Eigenvalue | 2.9 | |
| Variance explained (%) | 29.3 | |
| Cronbach’s alpha | 0.70 | |
Table 4
Judicial assistants’ influence on a concrete court case explained by principal-agent theory and contextual factors (N = 76, correlations [2-tailed significance], Betas).
| r | Beta (model 1)# | Beta (model 2)# | Beta (model 3)#,~ | |
|---|---|---|---|---|
| Principal-agent theory | ||||
| Managerial role orientation (hyp. 1) | 0.22* | 0.19* | – | 0.23* |
| Rule of law role orientation (hyp. 2) | –0.01 | 0.03 | – | 0.01 |
| Perceived risk-benefit (hyp. 3) | 0.45** | 0.23* | – | 0.21* |
| Trust (hyp. 4) | 0.62** | 0.45** | – | 0.43** |
| Relative experience of assistant (hyp. 5) | 0.27* | 0.12 | – | 0.15 |
| Contextual explanation | ||||
| Panel judgment (vs. single-judge) | 0.02 | – | 0.07 | 0.06 |
| Complexity case | –0.09 | – | –0.13 | –0.12 |
| Time pressure | 0.00 | – | 0.09 | 0.12 |
| R2% | 44.3** | 1.3 | 45.8** | |
[i] # In model 1 all principal-agent variables are regressed on the assistants’ influence, in model 2 all contextual factors are regressed on the assistants’ influence, in model 3 both the principal-agent variables and the contextual factors are regressed on the assistants’ influence.
~ The variance inflation factor (VIF) for the variables in model 3 ranges between 1.19 (rule of law role orientation) and 1.70 (complexity court case), all below the rule of thumb that if VIF>10 then multicollinearity is high63 (a cutoff of 5 is also commonly used).64
* p < 0.05, ** p < 0.01.