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This paper aims to discuss the application of Big Data analytics to the brainstorming session in the current auditing standards.

The authors review the literature related to fraud, brainstorming sessions and Big Data, and propose a model that auditors can follow during the brainstorming sessions by applying Big Data analytics at different steps.

The existing audit practice aimed at identifying the fraud risk factors needs enhancement, due to the inefficient use of unstructured data. The brainstorming session provides a useful setting for such concern as it draws on collective wisdom and encourages idea generation. The integration of Big Data analytics into brainstorming can broaden the information size, strengthen the results from analytical procedures and facilitate auditors’ communication. In the model proposed, an audit team can use Big Data tools at every step of the brainstorming process, including initial data collection, data integration, fraud indicator identification, group meetings, conclusions and documentation.

The proposed model can both address the current issues contained in brainstorming (e.g. low-quality discussions and production blocking) and improve the overall effectiveness of fraud detection.

This chapter combines quantitative studies of the connections between stressors and performance in accounting settings and identifies the mediators and moderators of stressors–performance relationships. Using meta-analyses and path analyses, this research compiles 72 studies to investigate the relationships of stressors with accountant and auditor performance. As hypothesized, bivariate meta-analyses results indicate that work-related stressors negatively affect performance, and burnout and stress are negatively related to performance, whereas motivation is positively related to performance. Moreover, a meta-analytical structural equation modeling indicates that role stressors have significant direct and indirect effects (through burnout and stress) on job performance. Accumulation of multiple samples through meta-analysis bolsters statistical power compared to single-sample studies and thus reveals the sign of residual direct effects of role stressors on job performance in accounting settings.

This study examines possible influences on the level of collaboration in published research by the most productive authors of accounting literature. Understanding the collaboration tendencies of these authors should benefit early-career-stage accounting faculty. Seven factors are examined for the publications of 93 of the most productive accounting authors. These productive authors are found to include fewer coauthors on their publications early in their careers. The number of coauthors increases through their first 16 to 17 years and then decreases through the remainder of their careers. The results also indicate that productive accounting researchers include a greater number of coauthors on more recently published articles and on longer articles. Fewer coauthors are included when a productive author is affiliated with a “top-10” university or on articles published in highly ranked accounting journals. Lastly, the results show that prolific authors seek out coauthorship throughout their careers and usually include one or more coauthors on their publications. Implications from these results and specific suggestions for accounting faculty are discussed.

This paper uses a laboratory experiment to examine the effect of accountability pressure as a monitoring control tool to mitigate subordinates' propensity to create budgetary slack. The results suggest that budgetary slack is (lowest) highest when accountability pressure is (present) absent under a private information situation. The results further reveal that accountability pressure is positively associated with subordinates' perceived levels of honesty, which in turn is negatively associated with budgetary slack creation. The findings of this paper have important theoretical and practical implications for budgetary control systems design.