Demographic variables

Demographic information is necessary to consider, since this information can confirm the comparability between various groups (Grove & Savich, 1979). The demographic data collected by this thesis includes gender, age, education, experience, firms and

region. Furthermore, these variables are considered as antecedents affecting the ethical

decision-making process, as suggested by Musbah et al. (2016) and Redfern and Crawford (2004). Thus, differences in the ethical decision-making process could be compared between groups for these variables. Gender is collected based on a nominal scale, which uses 1 to represent male and 2 to represent female. Other demographic variables are measured based on ordinal scales. The details of measurements for demographic variables are presented in Table 24.

Table 24: Measurements for demographic variables

Variables Categories Scale

Gender Male 1 = Male

Female 2 = Female Age 20–30 1 = 20–30 31–40 2 =31–40 41–50 3 =41–50 51–60 4 =51–60 Above 60 5 = Above 60

Education Junior college 1 = Junior college

Bachelor 2 = Bachelor

Master 3 = Master

Doctor 4 = Doctor

Experience 0–5 years 1 = 0–5 years

6–10 years 2 = 6–10 years

11–15 years 3 = 11–15 years

16–20 years 4 = 16–20 years

Over 20 years 5 = Over 20 years

Firms International Big 4 1 = International Big 4

China Big 10 2 = Chinese Big 8

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Variables Categories Scale

Region Northern (e.g., Beijing, Jinan) 1 = Northern Southern (e.g., Guangzhou,

Shenzhen) 2 = Southern

Eastern (e.g., Shanghai,

Hangzhou) 3 = Eastern

Central (e.g., Wuhan,

Zhengzhou) 4 = Central

Western (e.g., Xian, Lanzhou) 5 = Western

Others 6 = Others

5.3 Conclusion of Chapter 5

In this chapter, the research methodology utilised in this thesis was discussed. Methodology issues include the data collection method and measurement for each variable. A survey is employed in this thesis as the method for collecting data. The survey instrument includes an auditing case related to an auditor’s dilemma when faced with pressure from a client and a self-administered questionnaire. The measurement model for each variable is tested using CFA and the results show that the scales have high reliability and a satisfactory model fit for the data in this thesis. The statistical methods and results of this thesis are discussed in Chapter 6.

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Chapter 6: Results

This chapter presents the results of the statistical analysis for this thesis. The statistical methods used in this thesis include descriptive analysis, t-tests, Structural Equation Modelling (SEM), instrumental variable (IV) method and multi-group comparison of SEM. The descriptive analysis is presented first in Section 6.1. In this section, findings with respondents’ demographic information and descriptive analysis of variables are presented. Further, t-tests are utilised to compare variables within the model across demographic factors and the significant findings are presented in Section 6.2. The findings for the hypothesised relationships are examined through SEM analysis in Section 6.3. The findings for the multiple-group comparsions are presented in the Section 6.4

Mande, Ishak, Idris, and Ammani (2013) argue that the benefits of SEM are that this statistical method can test relationships between structural variables simultaneously, while other methods can only examine a single relationship at a time. Thus, SEM is the most appropriate method for examining multiple relationships among predictors and outcome factors within the conceptual framework of this thesis. According to Hulland (1999), procedures for SEM analysis have two stages: measurement model analysis and analysing the structural model. The tests of validity and reliability of each measurement model are completed through CFA (Chapter 5). Thus, SEM is applied in this chapter to examine the hypotheses in the structural model presented in Chapter 5 and summarised in Table 25. As the model in this thesis consists of two parts (the TPB model and the variables from the KP micro-level model), the results of the SEM tests will also be interpreted in two parts: the results of the TPB analysis (Section 6.3.1) and the SEM of decomposition of the TPB variables (Section 6.3.2).

Table 25: Hypotheses within the structural model Number Hypotheses

H1 Chinese auditors’ attitudes towards behaviour is positively associated with their

intention within the auditor-client context.

H2 Chinese auditors’ perception of subjective norms is positively associated with their

intention within the auditor-client context.

H3 Chinese auditors’ perceived behavioural control is positively associated with their

intention within the auditor-client context.

H4-1 Chinese auditors’ relativism orientation is negatively associated with their attitudes

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Number Hypotheses

H4-2 Chinese auditors’ idealism orientation is positively associated with their attitudes

towards behaviour within the auditor-client context.

H5-1 Chinese auditors’ perceived independence in mind is positively associated with

attitudes towards behaviour within the auditor-client context.

H5-2 Chinese auditors’ perceived independence in appearance is positively associated with

subjective norms within the auditor-client context.

H6 Within Chinese audit firms, perceived ethical climate is negatively associated with

subjective norms.

H7-1 Within Chinese audit firms, perceived rewards and punishments is positively associated

with subjective norms.

H7-2 Within Chinese audit firms, perceived manager’s unethical behaviour is negatively

associated with subjective norms.

H8-1 For Chinese auditors, their favour-seeking guanxi orientation is positively related with

subjective norms.

H8-2 For Chinese auditors, their rent-seeking guanxi orientation is negatively related with

subjective norms.

Endogeneity is an issue need to be examined when using observational data (Sande and Ghosh 2018). Wooldridge (2010) defines endogeneity as the situations that an explanatory variable in a regression equation correlates with the disturbance term. As Sande and Ghosh (2018) points out, endogeneity problems arise from three sources: omitted variables, simultaneity and measurement errors. They further recommend that IV method is the most commonly used approach to handle endogeniety issues in prior studies. Thus, in this thesis, IV method is also utilized to test whether endogeneity problems existing in conceptual frameworks. The results are presented in the Section 6.3.3.

Multi-group comparison through the SEM analysis is also employed in this thesis to investigate the influences of control variables. Cantisano, Domínguez, and García (2007) point out that research interests for social comparisons of people who face work-related threats have increased progressively. Through comparing decisions of different groups, the influence of social roles on an individual’s decision-making process can be investigated. In this thesis, the impact of the social role of Chinese auditors is investigated based on their demographic information. The SEM analysis is also used to test the hypotheses for each group and examine the differences between groups. Hence, the influences of control variables used in this thesis on the Chinese auditors’ ethical decision-making process will be also explored. The results of the

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multi-group comparison are presented in Section 6.4. Finally, Section 6.5 summarises the main findings of this chapter.