Search results

This research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.

This study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.

In this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.

The implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.

The model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.

The authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.

This study aims to explore the auditors’ responses to the COVID-19 pandemic in Egypt, with a focus on how their demographic characteristics – specifically gender, work experience and audit firm size – affect their ability to identify key audit matters (KAMs).

The study used exploratory factor analysis to develop an index for evaluating auditors’ proficiency in distinguishing KAMs from non-KAMs, followed by multivariate regression analysis to analyze the impact of auditors’ demographics on this ability.

The study’s findings are significant as they highlight the influence of auditors’ gender and work experience on their capability to correctly classify KAMs. However, the size of the audit firm showed no significant effect on the auditors’ decision-making efficacy in identifying KAMs.

While the study illuminates critical aspects of audit judgment during unprecedented times, it acknowledges limitations, including its geographical focus on Egypt and reliance on self-reported data. The implications stress the need for audit firms and regulators to consider auditors’ demographic characteristics when formulating policies to enhance audit quality and reliability during crises.

This research breaks new ground in the auditing literature by shedding light on the distinct role of auditor demographics in shaping audit opinion during crises. It is one of the pioneering studies to quantitatively assess the impact of auditors’ gender, experience and firm size on KAM identification in a global health crisis. It provides a unique perspective on audit practices in emerging economies.

This study aims to empirically investigate the impact of adopting big data and data analytics (BD&A) on audit quality (AQ).

A questionnaire was distributed among audit practitioners working at audit firms in Egypt and 205 responses were collected. Partial least square structural equation modeling (PLS-SEM) was used to analyze and test research hypotheses.

The results reveal that BD&A has a direct significant positive effect on the audit process (AP) and auditor competence (AC). However, an insignificant impact of BD&A is found on audit fees (AF). In addition, the results indicate that BD&A has significant positive direct and indirect impacts on AQ.

The results of this study will benefit several auditing stakeholders, such as audit firms, audit regulators, novice financial auditors and academic scholars.

This research is one of the earliest to empirically address the role of BD&A in enhancing AQ. It incorporates AP, AC and AF as mediators into a single model to explain the impact of BD&A on AQ. Also, it attempts to provide empirical evidence from a developing country with a less-regulated audit environment.