Directors' liability (IVOR nr. 101) 2017/3.4.2:3.4.2 Intermezzo: understanding subjective bad faith as a dependent variable

Directors' liability (IVOR nr. 101) 2017/3.4.2

3.4.2 Intermezzo: understanding subjective bad faith as a dependent variable

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Assuming ‘subjective bad faith’ to be a dependent variable, it becomes important to determine which behavioural and contextual legal case factors influence the court’s perception of a director’s action in ‘subjective bad faith’. Accordingly, I first applied bivariate analysis, the results of which are presented in Table 3.

Table 3. Bivariate associations between other legal case factors and director’s‘subjective bad faith’ (N=158)

Legal case factors

% subjective bad faith1

Exact Sig. (2-sided)

Cramér’s V

Behavioural factors:

1

0

Unreasonably informed

22%

49%

.000

.284

Foreseeability of damage

66%

16%

.000

.518

Unreasonable risk taking

22%

44%

.011

.207

Incompetence2

–

–

–

–

Dereliction of duty

24%

40%

.177

.120

Conflict of interest2

–

–

–

Norm violation

47%

30%

.031

.181

Limitation of liability

33%

42%

.267

.088

Contextual factors:

1

0

Misrepresentation of information

55%

31%

.006

.217

Fraud

45%

35%

.408

.080

Policy failure

29%

39%

.471

.071

Mismanagement

32%

39%

.562

.056

Bankruptcy

35%

52%

.161

.127

1

These involve the percentages of cases in which the court perceived a director’s ‘subjective bad faith’ under the condition that the legal case factor (predictor variable) occurred (=1) and did not occur (=0) in the cases under study.

2

This legal case factor did not meet the requirements of the chi-square test (all expected counts are greater than 1, and no more than 20% of the expected counts is less than 5).

Five legal case factors were found to have a significant effect on the court’s perception of a director’s action in ‘subjective bad faith’ (sig. ≤ 0.05). Of these 5 significant legal case factors, the factor ‘foreseeability of damage’ was found strongly associated with ‘subjective bad faith’ (V > 0.5). The other case factors were found weakly associated with ‘subjective bad faith’ (V < 0.3).

Further inspection shows that, in cases involving ‘foreseeability of damage’ on the part of the director, the court had affirmed that the director had acted in ‘subjective bad faith’ in 66% of them (Table 4).

Table 4. Crosstab foreseeability of damage by subjective bad faith (N=158)

Subjective bad faith

Exact Sig. (2-sided)

Yes

No

Total

Foreseeability

.000

Yes

66%

34%

43%

No

16%

84%

57%

To understand ‘subjective bad faith’ as a dependent variable more thoroughly, a logistic regression was applied, the results of which are shown in Table 5. On the basis of the Wald statistic, it can be said that all but one case factor (‘misrepresentation of information’) make a significant contribution to the prediction of the outcome (sig. ≤ 0.05): namely, whether or not the court discerned ‘subjective bad faith’ on the part of the director. It is clear from Table 5 that ‘foreseeability of damage’ (Wald = 31.597) is the strongest predictor compared to the other predictor variables. Based on the odds ratio (Exp(B)), it can be said that a director’s ‘foreseeability of damage’ leads to a court’s perception of a director’s ‘subjective bad faith’ with odds that are 14.70 times higher than in cases where there was no foreseeability of damage.

It is important to view these results with caution. In terms of effect size of the regression model, I obtained a Nagelkerke R square of 0.48. More broadly, the regression analysis was based on legal case factors. To understand ‘subjective bad faith’ in more depth requires further insight into ‘simple’ facts. In that respect, the model provides limited information.

Table 5. Logistic regression model: Subjective bad faith (N=158)

Legal case factors1

B

Wald2

Sig.

Exp(B)

Behavioural factors:

Foreseeability of damage

2.688

31.597

.000

14.699

Unreasonable information

-1.236

5.525

.019

.290

Norm violation

.907

4.012

.045

2.477

Unreasonable risk taking

-1.317

4.277

.039

.268

Contextual factors:

Misrepresentation of information

.849

2.632

.105

2.336

Constant

-1.736

16.540

.000

.176

Nagelkerke R square

0.484

Overall accuracy of the model

77.2%

1

All legal case factors have been tested for multicollinearity. The results yield no concerns.

2

The model was tested for robustness using the bootstrap method. The results do not give rise to different conclusions.

Based on the B-values in Table 5, it is possible to predict a hypothetical case with respect to a court’s perception of a director’s ‘subjective bad faith’. The formula is presented below.

Formula 1:

P = (1 / (1+e–logit))

logit = B0 + B1 * X1 + B2 * X2 + .. + Bn * Xn

Interestingly, the factor ‘foreseeability of damage’ seems to be a primer for a court’s perception of the director’s ‘subjective bad faith’. Applying Formula 1 to a hypothetical case involving ‘foreseeability of damage’ (and no other factors such as ‘unreasonable information’, ‘unreasonable risk taking’, ‘misrepresentation of information’, or ‘norm violation’ occurring in the case) yields a 72% chance that the court will attribute ‘subjective bad faith’ to a director:

logit = − 1.736 + 2.688 ∗ 1 − 1.236 ∗ 0 + 0.907∗ 0 − 1.317∗ 0 + 0.849 ∗ 0 = 0 .952

P = 1 / (1+e–0.952) ~ 0.72

Applying Formula 1 to a hypothetical case involving a ‘norm violation’ without the presence of other legal case factors results in a 30% chance that the court will attribute ‘subjective bad faith’ to a director.

The chance of discerning ‘subjective bad faith’ increases when more factors are in play, when there is, for example, ‘foreseeability of damage’ and the director ‘violated a norm’. Applying Formula 1, there is an 87% chance of the court attributing ‘subjective bad faith’ to a director’s action in such a case.

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