Descriptive results

The descriptive analysis is the first step of statistical analysis. Through descriptive analysis, the data is summarised and described with the findings. The descriptive results are presented in two parts: the first part provides the background information of respondents; and the second part presents the descriptive results for the dependent and independent variables.

6.1.1 Details of demographic information of respondents

The sample for this thesis included 366 responses in total. After removing 60 invalid responses, as discussed in Chapter 5, 306 usable responses were finally included in the sample. Female auditors represent 55.2% of the total sample. This finding is consistent with Zhao and Lord (2016), who reveal a high presence of women in the Chinese auditing profession. The majority of the respondents are aged between 20 and 30 years old (64.1%). There were 194 respondents (63.4%) with professional experience ranging from 1–5 years, 55 respondents (18%) with more than five years but less than ten years auditing experience, and 58 respondents (19%) with more than ten years working experience. Most of participants come from local firms (54.6%), and are located in the northern part of China (54.6%), though Woodbine et al. (2012) suggest that there is no significant difference in ethical intention between business regions. Details of respondents’ backgrounds are provided in Table 26.

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Table 26: Background information of respondents

Demographics Frequency Per cent

Gender Male 137 44.8 Female 169 55.2 Age 20–30 196 64.1 31–40 56 18.3 41–50 44 14.4 51–60 7 2.3 Above 60 3 1.0

Education Junior college 68 22.2

Bachelor 203 66.3 Master 33 10.8 Doctor 2 0.7 Experience 0–5 years 194 63.4 6–10 years 54 17.6 11–15 years 37 12.1 16–20 years 10 3.3 Over 20 years 11 3.6

Firms International Big4 20 6.5

Chinese Big8 119 38.9

Local small firms 167 54.6

Region Northern (e.g., Beijing, Jinan) 167 54.6

Southern (e.g., Guangzhou, Shenzhen) 12 3.9

Eastern (e.g., Shanghai, Hangzhou) 37 12.1

Central (e.g., Wuhan, Zhengzhou) 46 15.0

Western (e.g., Xian, Lanzhou) 26 8.5

Other 18 5.9

It is noted that some groups shown in Table 26 represent a small sample. For example, the PhD group has only two participants, and the group above 60 years of age only includes three participants. Considering that comparisons between groups are required to be implemented to address objective 3, which investigates the effects of demographic variables on ethical decision-making, regrouping of auditors based on demographic variables is necessary so that comparisons between groups are more efficient. Details of the regrouping of auditors are shown in Table 27.

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Table 27: Regrouping of participants

Demographics Frequency Per cent

Gender Male 137 44.8

Female 169 55.2

Age 20–30 196 64.1

Above 30 110 35.9

Education Junior college 68 22.2

Bachelor above 238 77.8

Experience 0–5 years 194 63.4

Above 5 years 112 36.6

Firms Big firms 139 45.4

Local small firms 167 54.6

Region North 193 63.1

South 113 36.9

For the variable gender, the samples are also categorised as male (137) and female (169), as before. For the variable age, the samples are regrouped as 20–30 (196) and above 30 (110), because Kohlberg (1969) indicates that an individual’s ethical reasoning level develops as they mature. Traymbak and Kumar (2018) classify adults aged between 20–30 as a young group. Similarly, Ebner, He and Johnson (2011) also categorise individuals younger than 30 years as young group. Schlegel (2008) further considers the age of 30 as a cut-off point, and indicates that adults aged 30 and above would become more reasoning. For the variable education, the samples are regrouped as junior college (68) and bachelor above (238). Wang and Calvano (2015) indicate that tertiary education is important for an auditor’s moral reasoning. Wang and Calvano (2015) further suggest that undergraduate education in codes of ethics would affect a student’s ethical decision-making. Hence, auditors are classified as the group of less than undergraduate education (i.e., junior college) and undergraduate education and above (i.e., tertiary education). For the variable experience, the samples are regrouped as 0–5 years (194) and above 5 years (112). Yustina and Putri (2017) consider auditors who have more than five years professional experience are experienced auditors. They posit that experienced auditors would be different from auditors who have less experience in ethical reasoning. Hence, this thesis adopts similar criteria to that used in Yustina and Putri’s (2017) study. For the variable firms, the samples are regrouped as big firms (139) and local small firms (167). Bagley et al.

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(2012) indicate that large accounting firms have better prestige and a more ethical firm climate, which could influence an auditor’s ethical decision-making. For the variable

region, the samples are regrouped as north (193) and south (113), due to the differences

in local cultures between south and north in China (Redfern & Crawford, 2004). Cliff (1995) also points out that north China is different from other regions in China, because regional culture in northern China is more influenced by Confucianism. Hence, northern and western group are combined as the north group, while southern, eastern, central and others groups are classified as the south group. Wei et al. (2014) point out that the geographic boundary between north and south in China is the Qingling Mountains – Huaihe River line.

6.1.2 Descriptive analysis of variables

The descriptive results, including overall mean, standard deviation, standard error mean and one-sample t-test results for all variables are presented in Table 27. These results show that mean scores for idealism, relativism, independence in mind,

independence in appearance, favour-seeking guanxi, ethical climate, manager’s unethical behaviour, rewards and punishments, attitudes towards behaviour, subjective norms and intention are significantly higher than the midpoint value 5 at 5%

significant level. The results also show that mean scores of rent-seeking guanxi and

perceived behavioural control are significantly lower than the midpoint value 5 at 5%

significant level.

Table 28: Descriptive and t-test of variables

Variables Mean Deviation Std. SE t Sig. (2-tailed)

Idealism 7.93 1.254 0.072 110.695 0.000 Relativism 5.57 2.063 0.118 47.224 0.000 Independence in mind 8.53 1.168 0.067 127.730 0.000 Independence in appearance 7.96 1.405 0.080 99.083 0.000 Favour-seeking guanxi 7.72 1.430 0.082 94.495 0.000 Rent-seeking guanxi 4.74 2.338 0.134 35.440 0.000 Ethical climate 6.95 1.411 0.081 86.157 0.000

Manager’s unethical behaviour 5.66 2.584 0.148 38.316 0.000 Rewards and punishments 7.00 1.805 0.103 67.830 0.000

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Variables Mean Deviation Std. SE t Sig. (2-tailed)

Attitudes towards behaviour 6.60 2.007 0.115 57.552 0.000

Subjective norms 4.44 2.186 0.125 35.509 0.000

Perceived behavioural control 4.99 2.278 0.130 38.339 0.000

Intention 6.36 2.135 0.122 52.083 0.000