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PurposeThe research paper investigates auditors' dysfunctional behaviors in relation to performance appraisals. It explores the dysfunctional audit behavior (DAB) differences among experienced/inexperienced and male/female auditors when expecting performance appraisals, how their perception of DAB changes and how their reactive-outcomes vary.Design/methodology/approachThe survey comprises statements pulled from performance appraisal templates used by the Big Four audit firms in the UK. The sample has been tested during two different periods to highlight variations in the perception of DAB.FindingsExperienced auditors become more tolerant of DAB compared to their perception when they were inexperienced. Inexperienced male auditors are generally more accepting of DAB compared to their inexperienced female counterparts. Experienced female respondents continue to be less accepting of DAB.Originality/valueThe study associates performance appraisal procedures with DAB. The analysis examines the perception of dysfunctional behavior according to the level of experience auditors (males and females) accumulate over time.

Large-scale datacenters consume megawatts in power and cost hundreds of millions of dollars to equip. Reducing the energy and cost footprint of servers can therefore have substantial impact. Web, Grid, and cloud servers in particular can be hard to optimize, since they are expected to operate under a wide range of workloads. For our upcoming datacenter, we set out to significantly improve its power efficiency, cost, reliability, serviceability, and environmental footprint. To this end, we redesigned many dimensions of the datacenter and servers in conjunction. This paper focuses on our new server design, combining aspects of power, motherboard, thermal, and mechanical design. We calculate and confirm experimentally that our custom-designed servers can reduce power consumption across the entire load spectrum while at the same time lower acquisition and maintenance costs. Importantly, our design does not decrease the servers' performance or portability, which would otherwise limit its applicability.

The study is concerned with the usefulness of using audit data analytics of unregulated voluntary disclosures in reducing the auditing expectations gap. It argues that the lack of credibility and assurance of the unstructured voluntary disclosures and other big data, will impact the level of public users' expectations towards the quality of these unregulated voluntary disclosures. Therefore, we argue that non-financial, as well as financial; data require assurance by an independent auditor. Consequently, this would expand the auditors' role and responsibilities which will lead to raising the degree of stakeholders' satisfaction and approaching their expectations potentially reducing the auditing expectations gap. Auditors will need to rely more heavily on big data analytics and technological techniques to perform this new role efficiently and effectively. Therefore, we provide empirical evidence that the perceptions of auditors, bankers, investors and academics, support the use of audit data analytics when providing assurance of unregulated voluntary disclosures in reducing auditing expectations gap. To do so, we categorized unregulated voluntary into 8 different categories that auditing data analytics is required to capture from various sources and analyze in an informative and useful fashion.