Comparison between two groups’ high skepticism auditors and low skepticism auditors| Statswork

Comparison between two groups’ high skepticism auditors and low skepticism auditors, and the impact of those groups in overall assessment of fraud risk

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Table of Contents Introduction ................................................................................................................................. 6 Test for Normality ....................................................................................................................... 6 Multi-Collinearity...................................................................................................................... 13 Common Method Variance (CMV) .......................................................................................... 15 Reliability .................................................................................................................................. 23 Independent sample t-test .......................................................................................................... 27 Analysis of Variance (ANOVA) ............................................................................................... 28 Chi-square tests ......................................................................................................................... 31 Correlation ................................................................................................................................. 33 Information ................................................................................................................................ 35 Analysis of Variance (ANOVA) ............................................................................................... 41 Correlation ................................................................................................................................. 43 Regression ................................................................................................................................. 48 References ................................................................................................................................. 56

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Table of Contents

Table 6: Frequency for highest level of education........................................................................ 20 Table 7: Frequency for professional position ............................................................................... 21 Table 8: Frequency for professional certificate ............................................................................ 22 Table 9: Descriptive statistics for experience ............................................................................... 23 Table 10: Reliability analysis for Skepticism dimensions ............................................................ 24 Table 11: Ranking for Skepticism dimension - Self determing.................................................... 24 Table 12: Ranking for Skepticism dimension - Curiosity ............................................................ 25 Table 13: Ranking for Skepticism dimension – Self confidence .................................................. 25 Table 14: Ranking for Skepticism dimension – Interpersonal skills ............................................ 26 Table 15: Ranking for Skepticism dimension – Delibrating ........................................................ 26 Table 16: Ranking for Skepticism dimension – Questioning skills.............................................. 27 Table 17: Mean difference in skepticism dimensions between gender ........................................ 28 Table 18: Mean difference in skepticism dimensions between experience .................................. 29 Table 19: Mean difference in skepticism dimensions between level of education....................... 30 Table 20: Mean difference in skepticism dimensions between professional position .................. 31 Table 21: Association between level of skepticism and gender ................................................... 32 Table 22: Association between level of skepticism and experience ............................................. 32 Table 23: Association between level of skepticism and level of education ................................. 33 Table 24: Association between level of skepticism and professional position ............................. 33 Table 25: Correlation between skepticism dimensions ................................................................ 34 Table 26: Descriptive statistics for financial ratios in High and Low risk information ............... 35 Table 27: Descriptive statistics for perception in High and Low risk information ...................... 36 Table 28: Descriptive statistics for judgment in High and Low risk information ........................ 37 Table 29: Descriptive statistics for decision in High and Low risk information .......................... 38 2 © 2016-2017 All Rights Reserved, No part of this document should be modified/used without prior consent Statswork™ - Your trusted mentor since 2001 I www.statswork.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@statswork.com

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Table 30: Reliability analysis for fraud risk (Low and High) factors in High and low risk ......... 39 Table 31: Mean difference in fraud risk (High and low) factors between gender ....................... 40 Table 32: Mean difference in fraud risk (High and low) factors between experience ................. 41 Table 33: Mean difference in fraud risk (High and low) factors between level of education ...... 42 Table 34: Mean difference in fraud risk (High and low) factors between professional position . 42 Table 35: Correlation between fraud risk factors ......................................................................... 43 Table 36: Reliability and validity estimates for information, perception and judgment ....... Error!

Bookmark not defined.

Table 37: Discriminant validity .....................................................Error! Bookmark not defined.

Table 38: Pathways coefficients for Information, Perception and JudgmentError!

Bookmark

not defined.

Table 39: Reliability and validity estimates for information, perception, judgment and Decision ....................................................................................................................................................... 45 Table 40: Discriminant validity .................................................................................................... 46 Table 41: Pathways coefficients for Information, Perception, Judgment and Decision ............... 46 Table 42: Reliability and validity estimates for information, judgment and Decision .......... Error!

Bookmark not defined.

Table 43: Discriminant validity .....................................................Error! Bookmark not defined.

Table 44: Pathways coefficients for Information, Judgment and DecisionError! Bookmark not defined.

Table 45: Reliability and validity estimates for perception and DecisionError! Bookmark not defined.

Table 46: Discriminant validity .....................................................Error! Bookmark not defined.

Table 47: Pathways coefficients for Perception and Decision.......Error! Bookmark not defined.

Table 48: Reliability and validity estimates for perception, judgment and Decision ............ Error!

Bookmark not defined.

Table 49: Discriminant validity .....................................................Error! Bookmark not defined.

Table 50: Pathways coefficients for Perception, Judgment and DecisionError! Bookmark not defined.

Table 51: Reliability and validity estimates for perception, judgment and Decision ............ Error!

Bookmark not defined.

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Table 52: Discriminant validity .....................................................Error! Bookmark not defined.

Table 53: Pathways coefficients for Perception, Judgment and DecisionError! Bookmark not defined.

Table 54: Reliability and validity estimates for perception, judgment and Decision ............ Error!

Bookmark not defined.

Table 55: Discriminant validity .....................................................Error! Bookmark not defined.

Table 56: Pathways coefficients for Perception, Judgment and DecisionError! Bookmark not defined.

Table 57: Correlation between high risk incentive, opportunity, attitude and overall risk decision ....................................................................................................................................................... 47 Table 58: Correlation between low risk incentive, opportunity, attitude and overall risk decision ....................................................................................................................................................... 48 Table 59: Association between high risk incentive, opportunity and attitude on overall risk decision ......................................................................................................................................... 49 Table 60: Association between low risk incentive, opportunity and attitude on overall risk decision ......................................................................................................................................... 50 Table 61: Mean difference in fraud risk factors between level of skepticism .............................. 51 Table 62: Mean difference in high fraud risk factors in low and high risk condition between level of skepticism ................................................................................................................................. 51

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List of Figures Figure 1: Percentage for gender .................................................................................................... 19 Figure 2: Percentage for full time audit experience ...................................................................... 20 Figure 3: Percentage for highest level of education...................................................................... 21 Figure 4: Percentage for Professional Position ............................................................................. 22 Figure 5: Percentage for Professional Certificate ......................................................................... 22 Figure 6: Scatter diagram for skepticism dimensions ................................................................... 35 Figure 7: Scatter diagram for high risk perception, judgment and decision ................................. 44 Figure 8: Scatter diagram for low risk perception, judgment and decision .................................. 44

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Introduction In this chapter the analysis performed are test for normality, Multi-Collinearity, reliability analysis, percentage analysis, descriptive statistics, t-test, analysis of variance, chi-square test, correlation, regression analysis and partial least square (PLS) using the software SPSS 20.0. The sample size taken for the study is N=42.

Test for Normality An assessment of the normality of data is a prerequisite for many statistical tests because

interested in the Tests of Normality table and the Histogram our numerical and graphical methods to test for the normality of data, respectively. When a histogram’s shape approximates a bell-curve it suggests that the data may have come for a normal population.

Histogram

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Tests of Normality

Adversely 0.161 42 Ineffective 0.151 42 Inadequate 0.171 42 Recurring 0.128 42 Rapid growth 0.141 42 Complex 0.128 42 Aggressive 0.120 42 Incentive 0.195 42 Opportunity 0.129 42 Attitude 0.126 42 Overall risk 0.176 42 Bolded value represents p>0.05

0.008 0.017 0.004 0.083 0.036 0.081 0.138 0.000 0.076 0.089 0.002

0.971 0.963 0.952 0.967 0.973 0.952 0.967 0.959 0.963 0.975 0.962

42 42 42 42 42 42 42 42 42 42 42

0.370 0.184 0.076 0.261 0.424 0.079 0.267 0.136 0.195 0.494 0.180

The above table presents the results from two well-known tests of normality, namely the Kolmogorov-Smirnov Test and the Shapiro-Wilk Test. The Shapiro-Wilk Test is more appropriate for small sample sizes (< 50 samples), but can also handle sample sizes as large as 2000.

From the above table for the "Domination", "Adversely", “Ineffective”, “Inadequate”, “Recurring”, “Rapid growth”, “Complex”, “Aggressive”, “Incentive”, “Opportunity”, “Attitude”, and "Overall risk" variables was normally distributed. If the Sig. value of the Shapiro-Wilk Test is greater than 0.05, the data is normal. If it is below 0.05, “Personal” variable the data significantly deviate from a normal distribution.

Multi-Collinearity Multicollinearity is a high degree of correlation (linear dependency) among several

is full ranked, making OLS impossible. When a model is not full ranked, that is, the inverse of X cannot be defined, there can be an infinite number of least squares solutions.

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There is no clear-cut criterion for evaluating multicollinearity of linear regression models.

condition number.

However, there is no formal criterion for determining the bottom line of the tolerance R2 be considered determining significance of multicollinearity. Klein (1962) suggests alternative criterion that R2k exceeds R2 of the regression model. In this vein, if VIF is greater than 1/(1-R2) value is less than (1-R2) Table 1: Tests of Multi-Collinearity Unstandardized Coefficients Model Beta S.E (Constant) 0.767 0.729 1 Perception-High risk 0.467 0.111 Judgment 0.500 0.094 Dependent Variable: Decision, **p<0.01

Collinearity t-value p-value Statistics Tolerance VIF 1.053 0.299 4.193 1.375 0.000** 0.727 5.339 1.375 0.000** 0.727

Table 1 presents the regression analysis for high risk in which dependent variable is decision and independent variables ate perception and judgment, from which multicollinearity is tested based on tolerance and VIF value. If the tolerance value less than .1 or VIF greater than 10 roughly indicates significant multicollinearity. In the above table tolerance value is 0.7 greater than 0.1 or VIF 1.4 which is less than 10.

Unstandardized Coefficients Beta S.E (Constant) 0.224 0.370 1 Perception-Low risk 0.394 0.106 Judgment 0.478 0.123 Dependent Variable: Decision, **p<0.01 Model

Collinearity t-value p-value Statistics Tolerance VIF 0.604 0.549 3.717 1.950 0.001** 0.513 3.900 1.950 0.000** 0.513

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Table 1 presents the regression analysis from which multicollinearity is tested based on

VIF 2.0 which is less than 10.

Common Method Variance (CMV)

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Harman single factor test using factor analysis Total Variance Explained Initial Eigenvalues Total % of Variance Cumulative % 1 5.066 38.972 38.972 2 1.632 12.556 51.528 3 1.385 10.653 62.181 4 .976 7.510 69.691 5 .865 6.657 76.348 6 .709 5.455 81.803 7 .650 5.002 86.805 8 .490 3.766 90.571 9 .409 3.150 93.721 10 .311 2.394 96.115 11 .198 1.524 97.639 12 .170 1.311 98.951 13 .136 1.049 100.000 Extraction Method: Principal Component Analysis.

Component

Extraction Sums of Squared Loadings Total % of Variance Cumulative % 5.066 38.972 38.972 1.632 12.556 51.528 1.385 10.653 62.181

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There are another way of testing common method bias in PLS see the link below: https://www.youtube.com/watch?v=pUKT-QvQYhM Please if you could perform this test I will be thankful it is very ease method I need to get attractive results in this method as my supervisor is giving me tough time because of this.

Table 2: CFA for First-order Model for fraud risk factors (Perception, Judgment and Decision) Unstandardized Standardized S.E coefficient coefficient per5 <--- perception 1.000 .128 per4 <--- perception 1.141 .511 .101 per3 <--- perception -.048 .623 -.004 per2 <--- perception .797 .355 .090 per1 <--- perception -22.365 98.050 -2.610 jud4 <--- judgment 1.000 .452 jud3 <--- judgment .917 .293 .337 jud2 <--- judgment .498 .236 .214 jud1 <--- judgment .510 .210 .238 per1 <--- Common_factor 1.062 .124 .682 per2 <--- Common_factor 1.062 .124 .660 per3 <--- Common_factor .879 .355 .395 per4 <--- Common_factor 1.062 .124 .517 per5 <--- Common_factor 1.062 .124 .748 jud1 <--- Common_factor 1.062 .124 .594 jud2 <--- Common_factor 1.143 .269 .586 jud3 <--- Common_factor 1.062 .124 .467 jud4 <--- Common_factor 1.062 .124 .574 dec4 <--- marker_(decision) 1.000 .151 dec3 <--- marker_(decision) 1.165 .691 .160 dec2 <--- marker_(decision) 2.254 1.287 .248 dec1 <--- marker_(decision) -.115 .703 -.016 dec1 <--- Common_factor 1.062 .124 .683 dec2 <--- Common_factor 1.062 .124 .536 dec3 <--- Common_factor 1.062 .124 .672 dec4 <--- Common_factor 1.062 .124 .738 Note: 1. ** Denotes significant at 1% level, NS- Not Significant Variables

P-value 0.026 0.939 (NS) 0.025 0.820 (NS) 0.002 0.035 0.015 *** *** 0.013 *** *** *** *** *** *** 0.092 (NS) 0.080 (NS) 0.870 (NS) *** *** *** ***

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to identify the degree to which the implicit matrix of co variances, (based on the hypothesized model), and the sample covariance matrix, based on data it seems to fit (Bollen, 1989).The structural model, (59)= 98.712, GFI the quality of fit was not acceptable representation of the sample data2 (χ (Goodness of Fit Index)=0.779; AGFI (Adjusted Goodness of Fit Index) = 0.659 which is smaller than the 0.90 criteria as suggested by Hu and Bentler (1999) and Joreskog and Sorbom (1981). Similarly, CFI=0.796, RMSEA (Root Mean Square Error of Approximation) =0.128 and RMR (Root Mean Square Residuals) =0.501, values are greater than 0.05 critical value (Steiger, 1989).

Table 3: Model fit indices for first order model constructs of fraud risk factors Indices p-value CMIN/DF GFI AGFI CFI RMR RMSEA

Suggested value >0.05 <5.0 >0.90 >0.90 >0.90 <0.05 <0.05

Model Fit Indices 0.001 1.67 0.779 0.659 0.796 0.501 0.128

Table 4: Frequency for Gender Gender Male Female Total

Frequency (n) 26 16 42

Percentage (%) 61.9 38.1 100.0

Table 2 presents the respondents’ gender. Of total 42 respondents’ majority (62%) of them are male and 38 percent of them are female.

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Figure 1: Percentage for gender

38% Male

62%

Female

Table 5: Frequency for full time audit experience Full time audit experience (years) <=2.5 2.6-5.0 5.1-17.0 >=17.1 Total

Frequency (n) 11 12 9 10 42 Mean

Percentage (%) 26.2 28.6 21.4 23.8 100.0 Range (Max-Min)

Full time audit experience (years)

11.7

45 (45-0)

Max- Maximum, Min-Minimum Table 3 shows the full time audit experience. Majority (29%) of them with 2.6-5.0 years’ experience followed by 26 percent with less than 2.5 years and least with 21 percent with 5.1-

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17.0 years’ experience respectively. The mean of full time audit experience is 11.7 years with maximum 45 years.

Figure 2: Percentage for full time audit experience 35 30

28.6

26.2

Percentage

25

23.8

21.4

20 15 10 5 0 2.6-5.0

<=2.5

>=17.1

5.1-17.0

Full time audit experience (years)

Table 6: Frequency for highest level of education Highest level of education Bachelors Masters Others Total

Frequency (n) 10 14 18 42

Percentage (%) 23.8 33.3 42.9 100.0

Table 4 reveals the highest level of education. Majority (43%) of them are rest of graduation followed by 33 percent with masters’ level and 24 percent with Bachelors level.

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Figure 3: Percentage for highest level of education

45 40

Percentage

35 30

42.9 33.3

25

Others

20

23.8

15

Masters Bachelors

10 5 0

Others

Masters

Bachelors

Highest level of education

Table 7: Frequency for professional position Professional position Staff Senior Supervisor Manager Partner Others Total

Frequency (n) 7 9 3 9 8 6 42

Percentage (%) 16.7 21.4 7.1 21.4 19.0 14.3 100.0

Table 5 shows the professional position. Majorities (21%) of them are senior and manager followed by 19 percent are partner, 17 percent are staff and least 7 percent of them are supervisor.

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Figure 4: Percentage for Professional Position

Professional Position

Supervisor

7.1

Others

14.3

Senior

Staff

16.7

Partner

19

Manager Partner

Manager

21.4

Senior

21.4 0

10

20

Staff Others Supervisor 30

Percentage

Table 8: Frequency for professional certificate Professional certificate ACCA CFA Others Total

Frequency (n) 21 3 18 42

Percentage (%) 50.0 7.1 42.9 100.0

Table 6 reveals the professional certificate. Majority (50%) of them have ACCA certificate followed by 43 percent have rest of certificate and 7 percent have CFA certificate.

Figure 5: Percentage for Professional Certificate

50 Percentage

40

50 42.9

30

ACCA Others

20 10 0

7.1 ACCA

Others

CFA

CFA

Professional Certificate

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Table 9: Descriptive statistics for experience Mean Experience in assessing the fraud risk (in months) 27.62 Number of audit engagement experienced 4.95 material fraud was discovered Experience working as an auditor (in years) 11.36 Max- Maximum, Min-Minimum

Range (Max-Min) 480 (480-0) 150 (150-0) 42 (42-0)

Table 7 presents the descriptive statistics for experience. The mean experience in assessing the fraud risk is 28 months with maximum of 480 months. The average number of audit engagement experienced material fraud was discovered is 5 with maximum of 150 audit engagement.The mean experience working as an auditor is 11 years with maximum 42 years.

Reliability The internal consistency of data is checked through cronbach’s alpha (α) value.

Cronbach’s alpha value is one of the measurements in reliability analysis. There is a relation between cronbach’s alpha and corr elation. Cronbach’s alpha generally increases when the correlation among the item increase.

Based on the Cronbach’s alpha value, we concluded the following about the data: If α ≥ 0.9 – Excellent If 0.7 ≤ α < 0.9 – Good If 0.6 ≤ α < 0.7 – Acceptable If 0.5 ≤ α < 0.6 –Poor If α < 0.5 - Unacceptable

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Table 10: Reliability analysis for Skepticism dimensions Number Range Mean S.D of items (Max-Min) Self determing 5 2.27 0.65 2.40 (3.40-1.00) Curiosity 5 4.87 0.76 2.80 (6.00-3.20) Self confidence 5 4.26 0.47 1.60 (5.00-3.40) Interpersonal skills 5 3.85 0.39 1.80 (4.60-2.80) Delibrating 5 4.90 0.56 2.20 (5.80-3.60) Questioning skills 5 4.59 0.59 2.60 (6.00-3.40) S.D- Standard Deviation; Max- Maximum, Min-Minimum Skepticism dimensions

Cronbach’s alpha 0.627 0.913 0.885 0.679 0.570 0.525

Table 8 presents the descriptive statistics for each dimensions and the reliability analysis outcome using the Cronbach’salpha method in order to measure the reliability of each multiitem of Skepticism dimensions (i.e. Self determing, Curiosity, Self-confidence, Interpersonal skills, Delibrating and Questioning skills) scale. From the Table 8, it is evident that there is a acceptable reliability with coefficient alphas ranging from 0.913 to 0.525.

Table 11: Ranking for Skepticism dimension - Self determing Self determing Mean I often accept other peoples’ explanations without further 5.07 thought.

I tend to immediately accept what other people tell me.

5.07 It is easy for other people to convince me.

4.64 I usually accept things I see, read or hear at face value.

4.60 Most often I agree with what the others in my group think.

4.29

S.D 0.78 1.22 0.88 1.15 1.07

Ranking 1.5 1.5 3 4 5

Table 11 shows the descriptive statistics along with ranking of each item of self determing. The ranking is based on the mean scores.

From the analysis, majority of the

respondents were agreed (Mean > 4) the following items namely, I often accept other peoples’ explanations without further thought, I tend to immediately accept what other people tell me, It is easy for other people to convince me, I usually accept things I see, read or hear at face value and Most often I agree with what the others in my group think. The item ‘ I often accept other peoples’ explanations without further thought and I tend to immediately accept what other people tell me’ had the first rank and ‘Most often I agree with what the others in my group think’ had the last rank.

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Table 12: Ranking for Skepticism dimension - Curiosity Curiosity Discovering new information is fun I like searching for knowledge I think that learning is exciting The prospect of learning excites me I relish learning

Mean 5.07

S.D 0.81

Ranking 1

4.98 4.86

0.75 0.93

4.79

1.00

2 3 4

4.64

0.91

5

Table 12 shows the descriptive statistics along with ranking of each item of curiosity.

The ranking is based on the mean scores.

From the analysis, majority of the respondents were

agreed (Mean > 5) the following items namely, Discovering new information is fun, I like searching for knowledge, I think that learning is exciting, The prospect of learning excites me and I relish learning. The item ‘ Discovering new information is fun’ had the first rank and ‘I relish learning’ had the last rank.

Table 13: Ranking for Skepticism dimension – Self confidence Self confidence I am confident of my abilities I have confidence in myself I don’t feel sure of myself I feel good about myself I am self-assured.

Mean 5.02

S.D 0.60

Ranking 1

4.95 4.88 4.76 4.45

0.80 1.09 1.01 1.09

2 3 4 5

Table 13 shows the descriptive statistics along with ranking of each item of self confidence. The ranking is based on the mean scores.

From the analysis, majority of the

respondents were agreed (Mean > 5) the following items namely, I am confident of my abilities, I have confidence inmyself, I don’t feel sure of myself, I feel good about myself and I am selfassured.. The item ‘ I am confident of my abilities’ had the first rank and ‘I am self-assured’ had the last rank.

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Table 14: Ranking for Skepticism dimension – Interpersonal skills Inter personal skills I am interested in what causes people to behave the way that they do I like to understand the reason for other peoples’ behavior The actions people take and the reasons for those actions are fascinating Other peoples’ behavior doesn’t interest me I seldom consider why people behave in a certain way

Mean

S.D

4.81

0.83

4.76

0.82

4.62

0.83

4.52 4.40

1.25 1.21

Ranking 1 2 3 4 5

Table 14 shows the descriptive statistics along with ranking of each item of Inter personal skills. The ranking is based on the mean scores.

From the analysis, majority of the respondents

were agreed (Mean > 4) the following items namely, I am interested in what causes people to behave the way that they do, I like to understand the reason for other peoples’ behavior , The actions people take and the reasons for those actions are fascinating, Other peoples’ behavior doesn’t interest meand I seldom consider why people behave in a certain way. The item ‘ I am interested in what causes people to behave the way that they do’ had the first rank and ‘I seldom consider why people behave in a certain way’ had the last rank.

Table 15: Ranking for Skepticism dimension – Deliberating Deliberating Mean I wait to decide on issues until I can get more information 5.21 I like to ensure that I’ve considered most available information 5.17 before making a decision I take my time when making decisions 4.98 I don’t like to decide until I’ve looked at all of the readily 4.79 available information.

I dislike having to make decisions quickly 4.33

S.D 0.57 0.76 0.84 0.95 1.32

Ranking 1 2 3 4 5

Table 15 shows the descriptive statistics along with ranking of each item of Delibrating.

The ranking is based on the mean scores.

From the analysis, majority of the respondents were

agreed (Mean > 4) the following items namely, I wait to decide on issues until I can get more information, I like to ensure that I’ve considered most available information before making a decision, I take my time when making decisions, I don’t like to decide until I’ve looked at all of the readily available information and I dislike having to make decisions quickly. The item ‘I wait to decide on issues until I can get more information’had the first rank and ‘I dislike having to make decisions quickly’ had the last rank.

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Table 16: Ranking for Skepticism dimension – Questioning skills Questioning skills I frequently question things that I see or hear I enjoy trying to determine if what I read or hear is true My friends tell me that I usually question things that I see or hear I often reject statements unless I have proof that they are true I usually notice inconsistencies in explanations

Mean 4.88 4.79

S.D 0.71 0.81

4.52

0.86

4.48

1.11

4.26

1.36

Ranking 1 2 3 4 5

Table 16 shows the descriptive statistics along with ranking of each item of Questioning skills. The ranking is based on the mean scores.

From the analysis, majority of the respondents

were agreed (Mean > 4) the following items namely, I frequently question things that I see or hear, I enjoy trying to determine if what I read or hear is true, My friends tell me that I usually question things that I see or hear, I often reject statements unless I have proof that they are true and I usually notice inconsistencies in explanations. The item ‘ I frequently question things that I see or hear’ had the first rank and ‘I usually notice inconsistencies in explanations’ had the last rank.

Independent sample t-test Independent sample t test is used to find out whether the mean of two unrelated groups (independent variable) are equal or not based on the same dependent variable. The data should be in the following format (Kent University, 2015; Laerd Statistics, 2015a).

Independent variable must be in categorical data Dependent variable should be measured on a continuous scale (interval or ratio) The data should contain without outliers. Outlier means a value(s) which is deviate from the whole data.

The data should follow approximately normally distributed for each of the group of independent variable.

The variability within the independent variable is not distinct.

F-value and p-value for Levene’s test are used to test the homogeneity of variances within two group of independent variable. Here p-value must be greater than 0.05, then we conclude that the two groups’ variance are same.

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Descriptive statistic (Mean, SD and SE) for two groups based on the dependent variable. t-value and p-value for t test are used for compare the mean of two groups based on dependent variable.

Table 17: Mean difference in skepticism dimensions between gender

Skepticism dimensions Self determing Curiosity Self confidence Interpersonal skills Delibrating Questioning skills

Gender (n=42) Male (n=26) Mean±SD 2.35±0.55 4.90±0.83 4.35±0.48 3.88±0.39 4.88±0.59 4.64±0.60

Female (n=16)

t-value

p-value

2.13±0.79 4.81±0.65 4.13±0.44 3.80±0.40 4.93±0.52 4.50±0.58

1.106 0.359 1.502 0.673 -0.268 0.739

0.276 0.722 0.141 0.505 0.790 0.464

An independent sample t test was performed comparing the mean ‘Skepticism dimensions’ between Male and Female . Table 15 results indicated that all the dimensions of ‘Skepticism’ namely Self determing (Male: M = 2.35, SD =0.55, Vs Female M= 2.13, SD=0.79, t= 1.106, p=0.276>0.05), Curiosity (Male: M = 4.90, SD =0.83, Vs Female M= 4.81, SD=0.65, t= 0.359, p=0.722>0.05), Self-confidence (Male: M = 4.35, SD =0.48, Vs Female M= 4.13, SD=0.44, t= 1.502, p=0.141>0.05), Interpersonal skills (Male: M = 3.88, SD =0.39, Vs Female M= 3.80, SD=0.40, t= 0.673, p=0.505>0.05), Delibrating (Male: M = 4.88, SD =0.59, Vs Female M= 4.93, SD=0.52, t= -0.268, p=0.790>0.05), and Questioning skills (Male: M = 4.64, SD = 0.60, Vs Female M= 4.50, SD=0.58, t= 0.739, p=0.464>0.05) were not significant in both the male and female.

Analysis of Variance (ANOVA) One way – ANOVA is one of the mean comparison test. This test is used to ascertain whether the mean of more than two groups (independent variable) are equal or not based on the dependent variable. The data should satisfy the following conditions (Laerd Statistics, 2015b; School of Public Health, 2015): Independent variable must be in categorical data 28

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Dependent variable should be measured on a continuous scale (interval or ratio) The data should contain without outliers. Outlier means a value(s) which is deviate from the whole data.

The data should follow approximately normally distributed for each of the group of independent variable.

The variability within the independent variable is not distinct.

Descriptive statistic (Mean, SD and SE) for groups based on the dependent variable.

F-value and p-value are used to compare the mean values between the groups of independent variable.

Multiple comparison tests give the information about whether combinations of two groups’ means are equal.

Table 18: Mean difference in skepticism dimensions between experience

Skepticism dimensions Self determing Curiosity Self confidence Interpersonal skills Delibrating Questioning skills **p<0.01

Experience in years (n=42) <=2.5 2.6-5.0 (n=11) (n=12) Mean±SD 2.44±0.54 2.30±0.68 5.16±0.56 5.35±0.59 4.31±0.56 4.35±0.40 3.85±0.48 4.02±0.28 5.00±0.53 4.97±0.64 4.55±0.71 4.55±0.45

5.1-17.0 (n=9)

>=17.1 (n=10)

F-value

p-value

2.13±0.80 4.51±0.56 4.16±0.49 3.71±0.25 5.00±0.26 4.67±0.66

2.16±0.64 4.28±0.81 4.20±0.47 3.78±0.48 4.60±0.65 4.60±0.60

0.453 6.915 0.370 1.221 1.251 0.085

0.717 0.001** 0.775 0.315 0.305 0.968

The obtained F-value and p-value for Curiosity are 6.915 and 0.001 respectively. Here pvalue is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between years of experience. In which Curiosity has a higher mean of 5.35 in 2.6-5.0 years compared to other years of experience.

The p-value is greater than 0.05 for self determing, Self-confidence, Interpersonal skills, Delibrating and Questioning skills. Hence there is no significant difference in mean self

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determing, Self-confidence, Interpersonal skills, Delibrating and Questioning skills between years of experience.

Table 19: Mean difference in skepticism dimensions between level of education

Skepticism dimensions Self determing Curiosity Self confidence Interpersonal skills Delibrating Questioning skills *p<0.05

Highest level of education (n=42) Bachelors Masters Others (n=18) (n=10) (n=14) Mean±SD 2.46±0.63 2.47±0.70 2.00±0.57 4.86±0.83 5.29±0.62 4.54±0.69 4.04±0.53 4.31±0.48 4.34±0.41 3.80±0.33 3.94±0.40 3.81±0.43 4.82±0.70 4.96±0.54 4.89±0.52 4.42±0.49 4.34±0.61 4.87±0.52

F-value

p-value

2.865 4.367 1.512 0.547 0.170 4.242

0.069 0.019* 0.233 0.583 0.844 0.022*

The obtained F-value and p-value for Curiosity are 4.367 and 0.019 and Questioning skills with F-value 4.242 and p-value 0.022 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between levels of education. In which Curiosity has a higher mean of 5.29 in masters compared to other level of education. In Questioning skills rest of education has higher mean of 4.87 compared to Bachelors and Masters.

The p-value is greater than 0.05 for self determing, Self-confidence, Interpersonal skills and Delibrating. Hence there is no significant difference in mean self determing, Selfconfidence, Interpersonal skills and Delibrating between levels of education.

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Table 20: Mean difference in skepticism dimensions between professional position Professional position (n=42) Skepticism Staff Senior dimensions (n=7) (n=9) Mean±SD Self determing 2.23±0.47 2.04±0.82 Curiosity 4.91±0.46 5.02±0.74 Self confidence 4.14±0.61 4.42±0.29 Interpersonal 3.77±0.39 3.89±0.48 skills Delibrating 5.03±0.60 4.80±0.56 Questioning 4.60±0.75 4.49±0.50 skills

Supervisor Manager Partner (n=3) (n=9) (n=8)

Others (n=6)

Fpvalue value

2.80±0.35 5.20±0.35 4.53±0.23

2.36±0.84 2.38±0.56 2.10±0.47 0.745 0.595 4.67±0.95 4.60±0.89 5.07±0.81 0.570 0.722 4.16±0.64 4.23±0.33 4.23±0.53 0.573 0.720

3.93±0.23

3.98±0.42 3.63±0.38 3.97±0.27 0.912 0.484

5.33±0.31

5.00±0.35 4.70±0.76 4.77±0.59 0.802 0.556

4.20±0.53

4.87±0.66 4.43±0.57 4.70±0.43 0.874 0.508

The p-value is greater than 0.05 for self determing, Curiosity, Self-confidence, Interpersonal skills, Delibrating and Questioning skills. Hence there is no significant difference in mean self determing, Curiosity, Self-confidence, Interpersonal skills Delibrating and Questioning skills between different Professional positions.

Chi-square tests The association between two categorical variables is tested through Chi-square test. The data should follow two conditions. (i)

The two variables should be in ordinal or nominal scale (i.e) categorical.

(ii)

The two variables consist of two or more independent groups.

Chi-square value and p-value give the information about whether there is an association between two categorical variables.

Phi-value and Cramer’s V are used to ascertain the strength of association between variables (Whether good association or bad association).

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Table 21: Association between level of skepticism and gender Gender (n=42) Level of Skepticism Male Female (n=26 ) (n=16) n(%) Low skepticism 12(46.2) 8(50.0) High skepticism 14(53.8) 8(50.0) Total 26(100.0) 16(100.0) Chi-square value: 0.059; p-value: 0.808>0.05

Total 20(47.6) 22(52.4) 42(100.0)

From Table 21 the obtained chi-square value and p- values are 0.059 and 0.808 respectively. Here, the p-value is greater than 0.05, and then we accept the null hypothesis H0 and support the alternative hypothesis H1. It concludes that there is no statistically significant association exists between level of Skepticism and Gender.

Table 22: Association between level of skepticism and experience

Level of Skepticism

Experience in years (n=42) <=2.5 2.6-5.0 (n=11) (n=12) n(%)

Low skepticism 4 (36.4) High skepticism 7 (63.6) Total 11 (100.0) Phi-value: 0.300 p-value: 0.287>0.05

4 (33.3) 8 (66.7) 12 (100.0)

5.1-17.0 (n=9)

>=17.1 (n=10)

Total

5 (55.6) 4 (44.4) 9 (100.0)

7 (70.0) 3 (30.0) 10 (100.0)

20 (47.6) 22 (52.4) 42 (100.0)

From Table 22 the obtained Phi-value and p- values are 0.300 and 0.287 respectively.

Here, the p-value is greater than 0.05, and then we accept the null hypothesis H0 and support the alternative hypothesis H1. It concludes that there is no statistically significant association exists between level of Skepticism and years of experience.

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Table 23: Association between level of skepticism and level of education Highest level of education (n=42) Level of Skepticism Bachelors Masters (n=10) (n=14) n(%) Low skepticism 5 (50.0) 7 (50.0) High skepticism 5 (50.0) 7 (50.0) Total 10 (100.0) 14 (100.0) Fisher exact value: 0.074; p-value: 0.938>0.05

Others (n=18)

Total

8 (44.4) 10 (55.6) 18 (100.0)

20 (47.6) 22 (52.4) 42 (100.0)

From Table 23 the obtained fisher exact value and p- values are 0.074 and 0.938 respectively. Here, the p-value is greater than 0.05, and then we accept the null hypothesis H0 and support the alternative hypothesis H1. It concludes that there is no statistically significant association exists between level of Skepticism and level of education.

Table 24: Association between level of skepticism and professional position Professional position (n=42) Level of Staff Senior Supervisor Skepticism (n=7) (n=9) (n=3) n(%) Low skepticism 3 (42.96) 4 (44.4) 1 (33.3) High skepticism 4 (57.1) 5 (55.6) 2 (66.7) Total 7 (100.0) 9 (100.0) 3 (100.0) Phi-value: 0.120; p-value: 0.988>0.05

Manager Partner Others (n=9) (n=8) (n=6)

Total

5 (55.6) 4 (50.0) 3 (50.0) 20 (47.6) 4 (44.4) 4 (50.0) 3 (50.0) 22(52.4) 9 (100.0) 8 (100.0) 6 (100.0) 42(100.0)

From Table 24 the obtained Phi-value and p- values are 0.120 and 0.988 respectively.

Here, the p-value is greater than 0.05, and then we accept the null hypothesis H0 and support the alternative hypothesis H1. It concludes that there is no statistically significant association exists between level of Skepticism and professional position.

Correlation The strength and direction of association between two variables are measured by Pearson correlation coefficient. The two variables must be measured on a continuous (interval) scale. The correlation coefficient (r) ranges from -1 to 1. Based on the sign of the correlation coefficient, it can be concluded as follows.

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If rX,Y is positive – The two variables X and Y are in positive relationship.

If rX,Y is negative – The two variables are in negative relationship.

If rX,Y = 0, There is no relationship between X & Y Significance (p) value is used to whether the two variables are in relationship or not.

Correlation coefficient (r) value is used to find out whether the two variables are positively relationship or negative relationship.

Table 25: Correlation between skepticism dimensions Skepticism Self Self Interpersonal Curiosity dimensions determing confidence skills Self 1 -0.003 -0.055 -0.086 determing Curiosity 1 0.174 0.364* Self 1 0.257 confidence Interpersonal 1 skills Delibrating Questioning skills **p<0.01, *p<0.05

Delibrating

Questioning skills

-0.114

-0.423**

0.341*

0.044

0.133

0.463**

0.243

0.338*

1

0.135 1

Table 25 present the Pearson correlation between Skepticism dimension variables respectively. The correlation values in the table are significant at the 1% and 5% level. There is significant negative correlation between self determing and Questioning skills (r=-0.423, p<0.01). The negative correlation indicates that the self determing increase the Questioning skill decreases. There is a significant positive correlation between Curiosity and Interpersonal skills (r=0.364, p<0.05), Curiosity and Delibrating (r=0.341, p<0.01). The positive correlation indicates that Curiosity increases Interpersonal skills and Delibrating also increases. There is a significant positive correlation between Questioning skills and Self-confidence (r=0.463, p<0.01), Questioning skills and Interpersonal skills (r=0.338, p<0.05). The positive correlation indicates that Questioning skills increases Self-confidence and Interpersonal skills also increases. The bolded value presents in Table 23 are either positive or negative significant correlation.

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Figure 6: Scatter diagram for skepticism dimensions

Information Table 26: Descriptive statistics for financial ratios in High and Low risk information Financial ratios Current ratio Net margin ratio Debt equity ratio

High risk information 2012 2011 2010 Mean 1.90 1.39 1.20 0.68 0.53 0.50 0.29 0.38 0.43

Low risk information 2012 2011 2010 Mean 1.54 1.40 1.27 0.56 0.53 0.50 0.35 0.38 0.43

Table 26 presents the descriptive statistics for financial information from 2010 to 2012 for high risk information. The mean current ratio is higher in 2012 (1.90) compared to 2010 and 2011. The mean net margin is higher in 2012 (0.68) compared to 2010 and 2011. The mean debt equity ratio is higher in 2010 (0.43) compared to 2011 and 2012.

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Similarly for low risk information the mean current ratio is higher in 2012 (1.54) compared to 2010 and 2011. The mean net margin is higher in 2012 (0.56) compared to 2010 and 2011. The mean debt equity ratio is higher in 2010 (0.43) compared to 2011 and 2012.

Table 27: Descriptive statistics for perception in High and Low risk information Perception

High risk perception Low risk perception Mean S.D Ranking Mean S.D Ranking

Personal financial obligations by ZZZs’ key 7.90 personnel Domination of management by a single person or small group without compensation 7.69 controls Inadequate monitoring controls, including automated controls and controls over interim 7.45 financial reporting Adverse relationship between company and employees Changes in compensation and

6.76

Promotions, compensation or other rewards inconsistent with expectations Ineffective communications, implementation, 6.74 support or enforcement of ethical climate

1.82 1

4.19

2.47 2

1.65 2

5.19

2.81 1

1.71 3

3.86

2.31 5

2.25 4

3.98

2.46 3

1.42 5

3.90

2.37 4

Table 27 shows the descriptive statistics along with ranking of each item of Perception.

The ranking is based on the mean scores.

From the analysis, majority of the respondents were

agreed (Mean > 7) in high risk perception the following items namely, Personal financial obligations by ZZZs’ key personnel, Domination of management by a single person or small group without compensation controls, Inadequate monitoring controls, including automated controls and controls over interim financial reporting, Adverse relationship between company and employees and Ineffective communications, implementation, support or enforcement of ethical climate. The item ‘ Personal financial obligations by ZZZs’ key personnel’ had the first rank and ‘Ineffective communications, implementation, support or enforcement of ethical climate’ had the last rank.

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compensation controls, Personal financial obligations by ZZZs’ key personnel, Adverse relationship between company and employees, Ineffective communications, implementation, support or enforcement of ethical climate and Inadequate monitoring controls, including automated controls and controls over interim financial reporting. The item ‘Domination of management by a single person or small group without compensation controls’ had the first rank and ‘Inadequate monitoring controls, including automated controls and controls over interim financial reporting’ had the last rank.

Table 28: Descriptive statistics for judgment in High and Low risk information High risk Judgment

Judgment

Mean S.D

Low risk Judgment

Ranking Mean S.D

Ranking

Aggressive or unrealistic forecasts by management in maintaining ZZZ's earnings 7.36 trend. Warranties expense in 2012 is £102,846, 6% increase from the previous year

1.87

1

3.98

2.05 1

Recurring management attempts to justify marginal or inappropriate accounting based 7.14 on materiality

1.62

2

2.95

1.70 4

Rapid growth or unusual profitability especially compared to that of the prior years. 6.55 See ratios analysis table

1.95

3

3.31

1.79 3

Complex or unstable organizational structure

2.14

4

3.55

2.40 2

5.40

Table 28 shows the descriptive statistics along with ranking of each item of Judgment.

The ranking is based on the mean scores.

From the analysis, majority of the respondents were

agreed (Mean > 5) in high risk judgment the following items namely, Aggressive or unrealistic forecasts by management in maintaining ZZZ's earnings trend. Warranties expense in 2012 is £102,846, 6% increase from the previous year, Recurring management attempts to justify marginal or inappropriate accounting based on materiality, Rapid growth or unusual profitability especially compared to that of the prior years and Complex or unstable organizational structure.

The item ‘Aggressive or unrealistic forecasts by management in maintaining ZZZ's earnings

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trend. Warranties expense in 2012 is £102,846, 6% increase from the previous year’ had the first rank and ‘Complex or unstable organizational structure’ had the last rank.

Similarly majority of the respondents were agreed (Mean > 3) in low risk judgment the following items namely, Aggressive or unrealistic forecasts by management in maintaining ZZZ's earnings trend. Warranties expense in 2012 is £102,846, 6% increase from the previous year, Complex or unstable organizational structure, Rapid growth or unusual profitability especially compared to that of the prior years. See ratios analysis table and Recurring management attempts to justify marginal or inappropriate accounting based on materiality. The item ‘Aggressive or unrealistic forecasts by management in maintaining ZZZ's earnings trend.

Warranties expense in 2012 is £102,846, 6% increase from the previous year’ had the first rank and ‘Recurring management attempts to justify marginal or inappropriate accounting based on materiality’ had the last rank.

Table 29: Descriptive statistics for decision in High and Low risk information Low risk Decision High risk Decision Mean S.D Ranking Mean S.D Ranking

Decision

What is the risk of financial statement fraud attributable to the incentives faced by ZZZs' 7.88 management What is the risk of financial statement fraud attributable to the opportunities available to 7.38 ZZZs

1.47

1

3.45

2.14 3

1.91

2.5

3.88

2.07 1

Based on all the information you have reviewed in this case, what is the overall risk 7.38 of material financial statement fraud for ZZZs

1.55

2.5

3.52

2.00 2

What is the risk of financial statement fraud attributable to ZZZs management’s attitude 7.31 or character

1.60

4

3.29

1.74 4

Table 29 shows the descriptive statistics along with ranking of each item of Decision.

The ranking is based on the mean scores.

From the analysis, majority of the respondents were

high (Mean > 7) in high risk decision the following items namely, What is the risk of financial statement fraud attributable to the incentives faced by ZZZs' management, What is the risk of financial statement fraud attributable to the opportunities available to ZZZs, Based on all the 38 © 2016-2017 All Rights Reserved, No part of this document should be modified/used without prior consent © 2016-2017 All Rights No- part this document should be Imodified/used without prior consent PhdReserved, Assistance™ Youroftrusted mentor since 2001 www.phdassisatnce.com UK: The Portergate, Ecclesall Road, Sheffield, S11since 8NX 2001 I UK #I +44-1143520021, Info@Phdassistance.com Statswork™ - Your trusted mentor www.statswork.com

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information you have reviewed in this case, what is the overall risk of material financial statement fraud for ZZZs and What is the risk of financial statement fraud attributable to ZZZs management’sattitude or character. The item ‘ What is the risk of financial statement fraud attributable to the incentives faced by ZZZs' management’ had the first rank and ‘What is the risk of financial statement fraud attributable to ZZZs management’sattitude or character’ had the last rank.

Similarly majority of the respondents were low (Mean > 2) in low risk decision the following items namely, What is the risk of financial statement fraud attributable to the opportunities available to ZZZs, Based on all the information you have reviewed in this case, what is the overall risk of material financial statement fraud for ZZZs, What is the risk of financial statement fraud attributable to the incentives faced by ZZZs' management and What is the risk of financial statement fraud attributable to ZZZs management’sattitude or character.

The item ‘What is the risk of financial statement fraud attributable to the opportunities available to ZZZs’ had the first rank and ‘What is the risk of financial statement fraud attributable to ZZZs management’sattitude or character’ had the last rank.

Table 30: Reliability analysis for fraud risk (Low and High) factors in High and low risk High risk Fraud Number Range risk of Mean S.D (Maxfactors items Min) 5 5.40 Perception 7.31 1.10 (9.404.00) 4 6.25 Judgment 6.61 1.31 (8.752.50) 4 6.25 Decision 7.49 1.21 (9.002.75)

Cronbach’s alpha 0.589 0.629 0.724

Low risk Number of Mean S.D items 5 4.22 1.97

Cronbach’s alpha

4

3.45

0.875

4

3.54

S.D- Standard Deviation; Max- Maximum, Min-Minimum

Range (MaxMin) 7.20 (8.401.20) 1.71 6.50 (7.751.25) 1.74 5.50 (7.001.50)

0.852

0.895

Table 30 presents the descriptive statistics for each fraud risk factors and the reliability analysis outcome using the Cronbach’salpha method in order to measure the reliability of each multi-item of fraud risk factors (i.e. Perception, Judgment and Decision) scale. From the Table

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30, it is evident that there is an acceptable reliability with coefficient alphas ranging from 0.72 to 0.589 in high risk and coefficient alphas ranging from 0.895 to 0.852 in low risk respectively.

Independent sample t-test Table 31: Mean difference in fraud risk (High and low) factors between gender Gender (n=42) Fraud risk factors

Male

Female

(n=26)

(n=16)

t-value

p-value

Mean±SD High risk Perception

7.40±1.09

7.16±1.14

0.674

0.504

Judgment

6.71±1.22

6.45±1.47

0.616

0.541

Decision

7.63±1.02

7.27±1.48

0.931

0.358

Perception

4.39±2.08

3.95±1.81

0.701

0.487

Judgment

3.73±1.88

2.98±1.32

1.391

0.172

Decision

3.99±1.89

2.80±1.17

2.263

0.029*

Low risk

*p<0.05 The obtained t-value and p-value for Decision in low risk are 2.263 and 0.029 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between male and female. In which Decision has a higher mean of 3.99 in male compared to female.

The p-value is greater than 0.05 for Perception, Judgment and Decision in high risk and Perception and Judgment in low risk. Hence there is no significant difference in mean Perception, Judgment and Decision in high risk and Perception and Judgment in low risk between male and female.

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Analysis of Variance (ANOVA) Table 32: Mean difference in fraud risk (High and low) factors between experience

Fraud risk factors

Experience in years (n=42) 5.1-17.0 <=2.5 2.6-5.0 (n=9) (n=11) (n=12) Mean±SD

High risk Perception 6.64±1.15 Judgment 6.68±1.62 Decision 7.09±1.73 Low risk Perception 4.56±1.63 Judgment 4.18±1.95 Decision 3.64±1.77 **p<0.01, *p<0.05

>=17.1 (n=10)

F-value

p-value

7.17±0.78 7.64±1.41 7.92±0.65 3.177 6.15±0.93 6.81±1.58 6.93±1.08 0.759 7.27±0.83 7.61±1.34 8.08±0.54 1.368

0.035* 0.524 0.267

5.22±2.00 3.18±1.99 3.60±1.84 2.562 4.27±1.64 2.33±1.10 2.65±1.09 4.550 4.58±1.76 2.58±1.32 3.03±1.53 3.039

0.069 0.008** 0.041*

The obtained F-value and p-value for Perception are 3.177 and 0.035. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between years of experience. In which Perception has a higher mean of 7.92 in >=17.1 years of experience compared to other years of experience.

The obtained F-value and p-value for Judgment are 4.550 and 0.008 and Decision with Fvalue 3.039and p-value 0.041 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between years of experience. In which Judgment has a higher mean of 4.27 in 2.6-5.0 years of experience compared to other years of experience. In Decision 2.6-5.0 years of experience has higher mean 4.58 compared to other years of experience.

The p-value is greater than 0.05 for Judgment and Decision in high risk and Perception in low risk. Hence there is no significant difference in mean Judgment and Decision in high risk and Perception in low risk between years of experience.

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Table 33: Mean difference in fraud risk (High and low) factors between level of education

Fraud risk factors High risk Perception Judgment Decision Low risk Perception Judgment Decision

Highest level of education (n=42) Bachelors Masters Others (n=18) (n=10) (n=14)

F-value

p-value

Mean±SD 7.38±0.89 7.17±1.02 7.38±1.30 0.158 6.60±1.40 6.48±1.53 6.72±1.13 0.127 7.25±0.90 7.34±1.47 7.74±1.17 0.663

0.854 0.881 0.521

4.16±1.43 4.89±2.17 3.74±2.02 1.347 3.63±2.13 3.64±1.55 3.19±1.64 0.332 3.93±1.81 3.84±1.74 3.08±1.70 1.074

0.272 0.719 0.352

The p-value is greater than 0.05 for Perception, Judgment and Decision in high risk and Perception, Judgment and Decision in low risk. Hence there is no significant difference in mean Perception, Judgment and Decision in high risk and Perception, Judgment and Decision in low risk between levels of education.

Table 34: Mean difference in fraud risk (High and low) factors between professional position

Fraud risk factors High risk Perception Judgment Decision Low risk Perception Judgment Decision

Professional position (n=42) Supervisor Manager Partner Staff Senior (n=3) (n=9) (n=8) (n=7) (n=9) Mean±SD

Others (n=6)

Fpvalue value

6.94±1.43 7.27±1.04 8.53±0.81 7.18±0.86 6.61±1.32 7.25±1.09 7.39±1.14 7.69±0.56 8.42±0.52

7.07±1.35 7.48±0.74 7.33±0.77 1.027 0.416 5.67±1.93 6.97±0.80 6.58±0.77 1.560 0.196 6.64±1.92 7.91±0.40 7.54±1.26 1.594 0.187

4.69±1.90 3.73±2.20 6.87±1.72 4.50±2.23 2.69±0.99 4.42±2.18 3.64±1.99 2.78±1.30 4.58±2.24

3.22±1.52 4.55±1.98 4.17±1.59 1.988 0.104 2.58±1.48 3.69±1.59 3.83±1.64 1.785 0.141 2.86±1.56 4.31±1.68 4.00±1.94 1.280 0.294

The p-value is greater than 0.05 for Perception, Judgment and Decision in high risk and Perception, Judgment and Decision in low risk. Hence there is no significant difference in mean

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Perception, Judgment and Decision in high risk and Perception, Judgment and Decision in low risk between professional positions.

Correlation Table 35: Correlation between fraud risk factors

High risk Perception Judgment Decision Low risk Perception Judgment Decision **p<0.01

Perception -High risk

Judgment Decision

PerceptionLow risk

Judgment Decision

1

.522** 1

.706** .761** 1

-.149 -.017 -.079

-.098 .103 .034

.087 .088 .072

-.149 -.098 .087

-.017 .103 .088

-.079 .034 .072

1

.698** 1

.774** .780** 1

Table 35 present the Pearson correlation between fraud risk variables respectively. The correlation values in the table are significant at the 1% and 5% level. There is significant positive correlation in high risk between Perception and Judgment (r=0.522, p<0.01), Perception and Decision (r=0.706, p<0.01) and Judgment and Decision (r=0.761, p<0.01). The positive correlation indicates that the Perception increases the Judgment and Decision also increases. There is a significant positive correlation in low risk between Perception and Judgment (r=0.698, p<0.01), Perception and Decision (r=0.774, p<0.01) and Judgment and Decision (r=0.780, p<0.01). The positive correlation indicates that the Perception increases the Judgment and Decision also increases. The bolded value presents in Table 33 are positive significant correlation.

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Figure 7: Scatter diagram for high risk perception, judgment and decision

Figure 8: Scatter diagram for low risk perception, judgment and decision

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Model 1: H1a:

Table 36: Reliability and validity estimates for information, perception, judgment and Decision Factors

Composite Reliability (CR > 0.7)

Average Variance Extracted (AVE > 0.5)

Leverage

R-square 0.427

Liquidity

1.000

1.000

0.522

Profitability

1.000

1.000

0.490

Perception

0.920

0.697

Judgment

0.921

0.746

0.727

Decision

0.958

0.851

0.877

Table 1 presents the reliability and validity estimates for information, perception, judgment and decision. From the above table all the factors satisfies the criteria for composite reliability 45 Š 2016-2017 All Rights Reserved, No part of this document should be modified/used without prior consent Statsworkâ&#x201E;˘ - Your trusted mentor since 2001 I www.statswork.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@statswork.com

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(C.R>0.7). All measures have a loading level above 0.70. In addition, measurement residuals are small. All loadings have the expected signs (i.e., non-negative) and are significant at the 0.001 level (one-tailed). Further, all constructs present a composite reliability (see, Fornell and Larcker 1981) above 0.70, the benchmark level suggested by Nunnally (1978) and Average Variance Extracted (AVE>0.5).

In all our models, average variance extracted (AVE) ranges between 0.70 and 1.00, indicating satisfactory convergent validity for the constructs.

Table 37: Discriminate validity Decision

Judgment

Perception

Leverage

Liquidity

Decision

0.9202

Judgment

0.903

0.863

Perception

0.884

0.824

0.835

Leverage

-0.754

-0.696

-0.653

Liquidity

0.804

0.732

0.723

-0.875

1.000

Profitability

0.787

0.715

0.700

-0.851

0.993

Profitability

1.000

Table 38: Pathways coefficients for Information, Perception, Judgment and Decision Pathways (regression weights)

Information, Perception, Judgment and Decision

p-values

Judgment Decision

0.545

0.000**

Perception Decision

0.434

0.000**

Perception Judgment

0.613

0.000**

Leverage Judgment

-0.191

0.180

Liquidity Judgment

0.005

0.993

Profitability Judgment

0.118

0.824

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Perception Leverage

-0.653

0.000**

Perception Liquidity

0.723

0.000**

Perception Profitability

0.700

0.000**

**p<0.01 The PLS path coefficients for information, perception , judgment and decision shown in Table 3.

Overall, our results suggest that judgment (β=0.545, p<0.01) and perception (β=0.434, p<0.01) have positive significant effect on Decision. Likewise perception have positive significant effect (β=0.613, p<0.01) on judgment and While perception have positive significant effect on two financial indicators liquidity and profitability and perception has negative significant effect on leverage. Thus model is supported.

H1 and H2a should be tested by using the full model above and by getting the pathways in the table above

H2b: There is a significant relationship between components assessment and overall assessment.

Table 39: Correlation between high risk incentive, opportunity, attitude and overall risk decision High risk

Incentive

Opportunity Attitude

Incentive Opportunity Attitude Overall risk decision **p<0.01, *p<0.05

1

.129 1

.452** .351* 1

Overall risk decision .579** .503** .444** 1

Table 57 present the Pearson correlation between incentive, opportunity, attitude and overall risk decision for high risk respectively. The correlation values in the table are significant at the 1% and 5% level. There is significant positive correlation in high risk between incentive and attitude (r=0.452, p<0.01), incentive and overall risk decision (r=0.579, p<0.01). The 47 © 2016-2017 All Rights Reserved, No part of this document should be modified/used without prior consent Statswork™ - Your trusted mentor since 2001 I www.statswork.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@statswork.com

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positive correlation indicates that the incentive increases the attitude and overall risk decision also increases. There is significant positive correlation between opportunity and attitude (r=0.351, p<0.01), opportunity and overall risk decision (r=0.503, p<0.01). The positive correlation indicates that the opportunity increases the attitude and overall risk decision also increases. There is significant positive correlation between attitude and overall risk decision (r=0.444, p<0.01). The bolded value presents in Table 55 are positive significant correlation.

Table 40: Correlation between low risk incentive, opportunity, attitude and overall risk decision Low risk

Incentive

Incentive 1 Opportunity Attitude Overall risk decision **p<0.01

Opportunity Attitude .566** 1

.683** .597** 1

Overall risk decision .756** .696** .836** 1

Table 58 present the Pearson correlation between incentive, opportunity, attitude and overall risk decision for low risk respectively. The correlation values in the table are significant at the 1% and 5% level. There is significant positive correlation in low risk between incentive and opportunity (r=0.566, p<0.01), incentive and attitude (r=0.683, p<0.01), incentive and overall risk decision (r=0.756, p<0.01). The positive correlation indicates that the incentive increases the opportunity, attitude and overall risk decision also increases. There is significant positive correlation between opportunity and attitude (r=0.597, p<0.01), opportunity and overall risk decision (r=0.696, p<0.01). The positive correlation indicates that the opportunity increases the attitude and overall risk decision also increases. There is significant positive correlation between attitude and overall risk decision (r=0.836, p<0.01). The bolded value presents in Table 56 are positive significant correlation.

Regression Multiple regression analysis is similar to the linear regression analysis. In the linear regression, we have to use only one independent variable and dependent variable. But in the multiple regressions, we can to use more than one independent variable and one dependent 48 Š 2016-2017 All Rights Reserved, No part of this document should be modified/used without prior consent Statsworkâ&#x201E;˘ - Your trusted mentor since 2001 I www.statswork.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@statswork.com

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variable. Both regression analyses are used to predict the value of a dependent variable based on the value of independent variable. Dependent variable means the variable we want to predict and independent variable means the variable we are using to predict the value of the dependent variable.

R square (R2) value explains what percent of variance in the dependent variable that can be explained by the independent variable.

F-ratio in the ANOVA table tells whether the overall regression model is a good fit or not for the data.

Estimated model coefficients table contains the following estimators: Through t-value and p-value for each independent variable, we can know whether each independent variable is significantly predicting the dependent variable. Beta (β) coefficients are the point estimator of independent variables. This table also contains the interval estimator of independent variable (Syke, 1993).

Table 41: Association between high risk incentive, opportunity and attitude on overall risk decision Independent variables

Unstandardized Coefficients Beta

S.E

R Square F-change

t-value

p-value

(Constant) 0.304 1.118 0.271 0.788 Incentive 0.517 0.132 3.919 0.000** 0.526 14.060 Opportunity 0.333 0.096 3.451 0.001** Attitude 0.075 0.128 0.587 0.561 Dependent Variable: Overall risk decision, S.E-Standard Error, **p<0.01 Table 59 presents the multiple regressions. The t-value and p-value for each independent variable tells whether the model is significantly predict the dependent variable or not. In this study, p-values are less than 0.05 for incentive (Beta=0.517, t=3.919, p<0.01) and opportunity (Beta=0.333, t=3.451, p<0.01) on overall risk decision in high risk, it reveals that two factors are used to predict the overall risk decision. Also the R-square value indicates 53 % of variation can be explained in the dependent variable that can be explained by these two factors. All the estimated values (β) are positive. Hence we concluded that, increasing the incentive and opportunity were associated with an increased overall risk decision.

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Table 42: Association between low risk incentive, opportunity and attitude on overall risk decision Independent variables

Unstandardized Coefficients Beta

S.E

R Square F-change

t-value

p-value

(Constant) -0.169 0.341 -0.496 0.623 Incentive 0.257 0.097 2.651 0.012** 0.797 49.873 Opportunity 0.230 0.091 2.524 0.016* Attitude 0.582 0.122 4.750 0.000** Dependent Variable: Overall risk decision, S.E-Standard Error, **p<0.01, *p<0.05 Table 60 presents the multiple regressions. The t-value and p-value for each independent variable tells whether the model is significantly predict the dependent variable or not. In this study, p-values are less than 0.05 for incentive (Beta=0.257, t=2.651, p<0.01), opportunity (Beta=0.230, t=2.524, p=0.016<0.05) and attitude (Beta=0.582, t=4.750, p<0.01) on overall risk decision in low risk, it reveals that three factors are used to predict the overall risk decision.

Also the R-square value indicates 80 % of variation can be explained in the dependent variable that can be explained by these three factors. All the estimated values (β) are positive. Hence we concluded that, increasing the incentive, opportunity and attitude were associated with an increased overall risk decision.

H3: Auditors (high and low skepticism) will give high assessment in high fraud risk condition when they are provided with SAS No. 99 fraud risk factors.

More clear hypothesis H3a: There are no significant differences between high and low skepticism auditors when assessing fraud risk in high fraud risk condition.

H3b: There are no significant differences between auditors with low skepticism and those with high skepticism in assessing fraud risk in low fraud risk condition.

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Table 43: Mean difference in fraud risk factors between level of skepticism

Fraud risk factors High risk Perception Judgment Decision Low risk Perception Judgment Decision *p<0.05

Level of Skepticism (n=42) Low High (n=20) (n=22) Mean±SD

t-value

p-value

7.49±1.06 7.15±1.14 1.013 6.75±1.51 6.49±1.12 0.641 7.43±1.38 7.55±1.07 -0.318

0.317 0.525 0.752

3.99±1.82 4.44±2.12 -0.728 2.89±1.32 3.95±1.88 -2.105 3.28±1.60 3.77±1.86 -0.923

0.471 0.042* 0.361

The obtained t-value and p-value for judgment in low risk are -2.105 and 0.042 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between low and high skepticism. In which judgment has a higher mean of 3.95 in high skepticism compared to low skepticism.

The p-value is greater than 0.05 for Perception, Judgment and Decision in high risk and Perception and Decision in low risk. Hence there is no significant difference in mean Perception, Judgment and Decision in high risk and Perception and Decision in low risk between low and high skepticism.

H3c: Low skepticism auditors give high assessment to fraud risk factors in high fraud risk condition.

H3d: High skepticism auditors give high assessment to fraud risk factors in high fraud risk conditions.

H3e: High skepticism auditors give low assessment to fraud risk factors in low fraud risk conditions.

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Fraud risk factors

Low risk condition tLevel of Skepticism value (n=42)

pvalue

Low High (n=20) (n=22) Mean±SD 5.10±2.79 5.27±2.88 -0.197 0.845

Domination of management by a single person or small group without compensation controls Personal financial 4.05±2.56 4.32±2.44 -0.347 0.730 obligations by ZZZs’ key personnel.

Adverse 3.75±2.47 4.18±2.50 -0.562 0.577 relationship between company and employees

High risk condition tpvalue value Level of Skepticism (n=42) Low High (n=20) (n=22) Mean±SD 7.45±1.47 7.91±1.80 -0.901 0.373

8.30±1.56 7.55±1.99 1.357

0.183

6.95±2.35 6.59±2.20 0.512

0.612

3.50±2.09 4.18±2.50 -0.953 0.361

7.55±1.76 7.36±1.71 0.348

0.730

3.55±2.14 4.23±2.56 -0.925 0.361

7.20±1.40 6.32±1.32 2.099

0.042*

Changes in compensation Promotions, compensation or other rewards inconsistent with expectations Inadequate monitoring controls, including automated controls and controls over interim financial reporting Ineffective communications, implementation, support or enforcement of ethical climate

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Recurring 2.60±1.05 3.27±2.10 -1.295 0.203 7.50±1.54 6.82±1.65 1.380 0.175 management attempts to justify marginal or inappropriate accounting based on materiality Rapid growth or 2.70±1.26 3.86±2.03 -2.204 0.033* 6.30±2.03 6.77±1.90 -0.780 0.440 unusual profitability especially compared to that of the prior years Complex or 2.80±2.02 4.23±2.56 -1.992 0.053* 5.85±2.23 5.00±2.02 1.295 0.203 unstable organizational structure Difficulty in determining organization or individuals with control of company Overly complex structure Aggressive or 3.45±1.85 4.45±2.15 -1.614 0.114 unrealistic forecasts by management in maintaining ZZZ's earnings trend.

Warranties expense in 2012 What is the risk of 3.55±2.28 4.45±2.15 0.278 0.782 financial statement fraud attributable to the incentives faced

by ZZZs' management?

What is the risk of 3.50±1.79 4.23±2.29 -1.139 0.261 financial statement fraud attributable to the opportunities available to ZZZs

7.35±2.13 7.36±1.65 -0.023 0.982

7.85±1.66 7.91±1.31 -0.129 0.898

7.05±1.99 7.91±1.31 -1.071 0.290

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What is the risk of 3.05±1.54 3.50±1.92 -0.832 0.410 financial statement fraud attributable to ZZZs management’s attitude or character?

Based on all the 3.00±1.72 4.00±2.16 -1.650 0.107 information you have reviewed in this case, what is the overall risk of material financial statement fraud for ZZZs? *p<0.05

7.45±1.36 7.18±1.82 0.538

0.594

7.35±1.81 7.41±1.30 -0.122 0.903

The obtained t-value and p-value for Rapid growth in low risk are -2.204 and 0.033 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between low and high skepticism in assessing this factor. In which Rapid growth has a higher mean of 3.86 in high skepticism compared to low skepticism. The obtained t-value and p-value for complex or unstable in low risk are -1.992 and 0.053 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between low and high skepticism. In which complex or unstable has a higher mean of 4.23 in high skepticism compared to low skepticism Similarly the obtained t-value and p-value for ineffective in high risk are 2.099 and 0.042 respectively. Here p-value is less than 0.05, it leads to reject the null hypothesis and support the alternative hypothesis. Hence, there is a statistically significant difference between low and high skepticism. In which ineffective has a higher mean of 7.20 in low skepticism compared to high skepticism The p-value is greater than 0.05 for domination, personal, adversely, ineffective, inadequate, recurring, aggressive, incentive, opportunity, attitude and overall risk in low risk condition and domination, personal, adversely, inadequate, recurring, aggressive, incentive, opportunity, attitude and overall risk in high risk condition in low risk. Hence there is no significant difference in mean domination, personal, adversely, ineffective, inadequate, 54 © 2016-2017 All Rights Reserved, No part of this document should be modified/used without prior consent Statswork™ - Your trusted mentor since 2001 I www.statswork.com UK: The Portergate, Ecclesall Road, Sheffield, S11 8NX I UK # +44-1143520021, Info@statswork.com

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recurring, aggressive, incentive, opportunity, attitude and overall risk in low risk condition and domination, personal, adversely, inadequate, recurring, aggressive, incentive, opportunity, attitude and overall risk in high risk condition and in low risk between low and high skepticism.

Which suggest that there are no significant differences between high and low skepticism auditors in assessing fraud risk in high and low fraud risk conditions.

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