Search results

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 study investigates whether and how chief executive officers (CEOs) with personal risk-taking preference (expressed in owning a pilot license) will act differently when they are vested with additional power serving as board chairs.

Regressions analyses are performed using a sample of Standard and Poor’s (S&P) 1,500 firms with available data during 1996–2009. CEO's risk-taking outcomes are measured using firms' total risk, idiosyncratic risk and research and development expenditures (R&D) investment.

Firms led by pilot CEOs have greater firm risks, yet CEO duality attenuates the relationship. Further channel tests show that CEO duality suppresses CEO's risk-taking tendencies through managers' reputation concerns.

The findings highlight the importance of incorporating human factors into consideration of appropriate governance structures for a firm. Future studies can expand the existing data and further explore the relationship between human factors and governance structures on other firm strategies.

Regulators may focus mainly on regulatory setting based on the “best practice” of governance yet overlook human influence in corporate dynamics. For shareholders, hiring managers with distinct styles will change corporate outcomes but different governance mechanisms could be devised to adapt to CEOs with various personalities.

Prior studies show that both CEO personal preferences and firms' governance structure affect corporate policies, and this paper complements prior studies by exploring how the two may interact to shape corporate policy and its outcomes. This paper also adds to the literature showing that CEO duality could serve a disciplinary role.

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.

In this paper we propose a strategy for investing in new companies for which there is relatively little hard data available. We use fuzzy set theory to represent these new companies as finite fuzzy subsets of established companies for which there is a history of investment data. A fuzzy set is also used to represent the economic environment in which the proposed new investments will be made. From this fuzzy information we construct a fuzzy expected return for each new investment under consideration. These expected returns are then defuzzified, and those proposed investments whose defuzzified expected returns fail to meet some specified criteria are discarded. An investment strategy is then proposed for investing available capital in those new companies that meet the criteria.