(eA
η at two levels (i.e., less than one vs. one) to reflect the fundamental difference between NE and ST regimes.
dThe subject pool consists of 80 senior Business School students, with 10 auditor-subjects and 10 manager-subjects randomly assigned to each experiment. All subjects draw to determine the role they will play in the experiments. At the beginning of each period, each manager-subject is endowed with 12,000 EDs and each auditor-subject is endowed with 10,000 EDs. Each subject plays the same role throughout all 70 periods.
TABLE 4
Players' Overall Behavior in Four Experimentsa Panel A-1: Mandatory Forecast − Manager’s Behavior
f (FChigh )
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(A) Prediction Averageb
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(C) Prediction Average b
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(E) Prediction Averageb
TABLE 4
Players' Overall Behavior in Experiments (cont’d) Panel A-2: Mandatory Forecast − Auditor’s Behavior
f (ehighA )
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(G) Prediction Averageb
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(I) Prediction Averageb
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(K) Prediction Averageb
TABLE 4
Players' Overall Behavior in Experiments (cont’d) Panel B-1: No Mandatory Forecast − Manager’s Behavior
f (Ihigh)
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(M) Prediction Averageb
TABLE 4
Players' Overall Behavior in Experiments (cont’d) Panel B-2: No Mandatory Forecast − Auditor’s Behavior
f (ehighA )
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(Q) Prediction Averageb
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(S) Prediction Averageb
Negligence Regime (NE) Strict Regime (ST) Experiment
Ordersa Prediction Averageb
(U) Prediction Averageb
aThis study adopts a split-plot factorial design, with one between-subject variable, REGIME (manipulated at two levels: NE vs. ST), and one within-subject variable, RULE (manipulated at two levels: JS vs. PR). Each experiment consists of 35 periods of treatment JS and 35 periods of treatment PR participated by 20 subjects (10 auditors and 10 managers). To minimize the carryover effect, two RULE orders are manipulated for each REGIME level: JS_PR (Order #1) and PR_JS (Order #2). Four dependent variables are of particular interest: (a) f(auditor’s high effort), which denotes the frequency of auditor’s choosing high effort level, measured by the ratio of the number of periods each auditor-subject chooses high effort level divided by 35, (b) f(auditor’s independence audit failure), which denotes the frequency of auditor’s committing an audit failure due to a lack of independence, measured by the ratio of the number of periods each auditor-subject issues an Hˆ report when he observes an SL signal divided by the total number of periods the subject obtains an SL signal, no matter whether there is a lawsuit against the auditor due to a bad state of economy. (c) f(auditor’s technical audit failure), which denotes the frequency of auditor’s committing a technical audit failure, measured by the ratio of the number of periods each auditor-subject obtains an SH signal when the true investment outcome is L divided by the total number of periods the subject obtains an SH signal, no matter whether there is a lawsuit against the auditor, and (d) f(manager’s high investment), which denotes the frequency of manager’s choosing a high investment project, measured by the ratio of the number of periods each manager-subject chooses high investment divided by 35.
bStandard deviations are reported in parentheses.
cSince each subject is given two different RULE treatments in each experiment, his / her responses under treatments JS and PR are dependent (or correlated). Therefore, the matched-sample t tests are used to examine whether subjects’ decisions are different under both rules. Asterisks *, **, and *** indicate significance of the t-statistic at the 0.10, 0.05, and 0.01 levels, two-tailed, respectively.
TABLE 5
Tests of Subjects’ Reputation Concerns (Hypothesis H5) and Ethical Concerns (Hypothesis H6) Panel A: Test of Hypothesis H5 (Reputation Concerns)
(1) Descriptive Statistics (n = 40):
Variables Mean Median Std. Dev. Max. Min.
FH_IHa 0.7380 0.7083 0.1188 1.0000 0.6000 REPUTATION b 5.2500 5.0000 1.1491 7 3 LOSS c 171 0 50 667 0 (2) Bivariate Statistics between FH_IH and REPUTATION: d
Pearson correlation: 0.4581***
Spearman correlation: 0.4167***
(3) Bootstrap-estimated Parameters of Regression Model:
FH_IH = a + b1(REPUTATION) + b2(LOSS) + b3(REGIME) + b4(ORDER) + ε (H5: b1 > 0)
Panel B: Test of Hypothesis H6 (Ethical Concerns) (1) Descriptive Statistics (n = 20):
Variables Mean Median Std. Dev. Max. Min. (3) Bootstrap-estimated Parameters of Regression Model:
AFind = a + c1(ETHIC) + c2(LOSS) + c3(ORDER) + ε (H6: c1 < 0)
aVariable FH_IH is measured by the frequency of each manager-subject’s undertaking Ihigh, given he / she forecasts FChigh, under both legal regimes.
bVariable REPUTATION is measured by each subject’s response to one statement in the post-experimental questionnaire, which ranges from 1 (strongly disagree) to 7 (strongly agree).
cVariable LOSS is measured for each manager-subject by multiplying Chighlow(i.e., 1,000 EDs) by the ratio of the number of periods each manager-subject receives an Lˆ (given FChigh, Ihigh, L, RH) divided by the total number of periods each manager-subject reports RH (given FChigh, Ihigh, L).
dAsterisks ‘*’, ‘**’, and ‘***’ indicate the two-tailed significance of the correlation coefficient at the 0.1, 0.05, and 0.01 levels.
eThe bootstrap-estimated parameters are computed from resampling of the original data. To test the significance of the variables of interest, confidence limits are calculated using the bias-corrected and accelerated (BCa) method and are given three values of the significance level for each behavioral hypothesis: 1%, 5%, and 10% for H5 (one-tailed test of b1 > 0) and 90%, 95%, and 99% for H6 (one-tailed test of c1 < 0). To achieve acceptable accuracy of the confidence limits, all parameters are estimated with 30,000 replications.
fVariable REGIME denotes the between-subject variable, which is manipulated at two levels: NE and ST.
gVariable ORDER denotes the two FORECAST orders that are manipulated for each REGIME level in the experiments:
MANDATORY_NO (Order #1) and NO_MANDATORY (Order #2).
hVariable AFind is measured by the average of the two AFind rates across two FORECAST levels under the Negligence legal regime for each auditor-subject.
iVariable ETHIC is measured by each subject’s response to one statement in the post-experimental questionnaire, which ranges from 1 (strongly disagree) to 7 (strongly agree).
jI first calculate each auditor-subject’s “mean damage loss” by multiplying Dind (i.e., 11,200 EDs) by the ratio of the number of periods each auditor-subject is held liable for an AFind divided by the total number of periods each auditor-subject compromises independence under each FORECAST level. Variable LOSS is then measured by the average of each auditor-subject’s “mean damage loss” across two FORECAST levels.
TABLE 6
Repeated-Measured ANOVA Resultsa (n = 40)
Panel A: ANOVA on the Mean Frequency of Manager’s Undertaking High Investment
Source of Variation SS df MS F statisticb p value Between-Subject
REGIME 1.513 1 1.513 138.541 < 0.000 Subject-within-groups 0.415 38 0.011
Within-Subject
FORECAST 1.452 1 1.452 211.808 < 0.000 REGIME × FORECAST 0.888 1 0.888 129.501 < 0.000 FORECAST × Subject-within-groups 0.261 38 0.007
Panel B: ANOVA on the Mean Frequency of Manager’s Reporting Low when Outcome is Low:
Source of Variation SS df MS F statisticb p value Between-Subject
REGIME 5.206 1 5.206 393.832 < 0.000 Subject-within-groups 0.502 38 0.013
Within-Subject
FORECAST 0.068 1 0.068 7.572 < 0.009 REGIME × FORECAST 0.004 1 0.004 0.418 < 0.522 FORECAST × Subject-within-groups 0.344 38 0.009
Panel C: ANOVA on the Mean Frequency of Auditor’s Choosing High Effort Level
Source of Variation SS df MS F statisticb p value Between-Subject
REGIME 5.364 1 5.364 451.425 < 0.000 Subject-within-groups 0.451 38 0.012
Within-Subject
FORECAST 0.070 1 0.070 6.951 < 0.012 REGIME × FORECAST 0.001 1 0.001 0.049 < 0.825 FORECAST × Subject-within-groups 0.384 38 0.010
TABLE 6
Repeated-Measured ANOVA Results (cont’d) (n = 40)
Panel D: ANOVA on the Mean Frequency of Auditor’s Committing Independence Audit Failure
Source of Variation SS df MS F statisticb p value Between-Subject
REGIME 5.047 1 5.047 300.637 < 0.000 Subject-within-groups 0.638 38 0.017
Within-Subject
FORECAST 0.047 1 0.047 1.790 < 0.189 REGIME × FORECAST 0.000 1 0.000 0.000 < 0.991 FORECAST × Subject-within-groups 1.008 38 0.027
Panel E: ANOVA on the Mean Frequency of Auditor’s Committing Technical Audit Failure
Source of Variation SS df MS F statisticb p value Between-Subject
REGIME 1.565 1 1.565 1074.738 < 0.000 Subject-within-groups 0.055 38 0.001
Within-Subject
FORECAST 0.835 1 0.835 897.869 < 0.000 REGIME × FORECAST 0.497 1 0.497 523.668 < 0.000 FORECAST × Subject-within-groups 0.036 38 0.001
aThis study adopts a split-plot factorial design, with one between-subject variable, REGIME (manipulated at two levels: NE vs. ST), and one within-subject variable, RULE (manipulated at two levels: JS vs. PR). Each experiment consists of 35 periods of treatment JS and 35 periods of treatment PR participated by 20 subjects (10 auditor-subjects and 10 manager-subjects).
bIn calculating the F statistics, the degree of freedom is adjusted by the Huynh-Feldt procedures. This adjustment (also called the Box adjustment) is usually recommended in repeated measures design when researchers have reservations about making the sphericity assumption in using the F test.
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APPENDIX
Players’ Payoffs under Different Legal systems and Game Outcomes
Panel A: Forecast High (FH)_Strict Regime (ST)
Game Outcomes Manager Auditor
FH
Panel B: Forecast Low (FL)_Strict Regime (ST)
Game Outcomes Manager Auditor
FL
APPENDIX
Players’ Payoffs under Different Legal systems and Game Outcomes (cont’d)
Panel C: Forecast High (FH)_Negligence Regime (NE)
Game Outcomes Manager Auditor
FH
Panel D: Forecast Low (FL)_Negligence Regime (NE)
Game Outcomes Manager Auditor
FL
APPENDIX
Players’ Payoffs under Different Legal systems and Game Outcomes (cont’d)
Panel E: No Forecast (NF)_Strict Regime (ST)
Game Outcomes Manager Auditor
NF
Panel F: No Forecast (NF)_Negligence Regime (NE)
Game Outcomes Manager Auditor
NF
aSee Table 1 for the definitions and parameter values of all variables shown in this Table.
bSince λ(eA) and η(eA) equal one under the strict legal regime, game outcomes D and I in Panels A, B, and E do not exist.