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The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

This study aims to explore the provision of accounting ethics education in Ethiopian accounting programs through structuration theory.

A qualitative case study methodology was used, and data were collected through semistructured interviews with faculty members, practitioners and regulatory bodies. The interview transcripts were analyzed using thematic analysis. Accounting curricula were also analyzed to triangulate and support the findings.

The study revealed a significant gap in the status of accounting ethics education, primarily attributed to a misunderstanding of its importance. While faculty initiatives and student interest have partially mitigated this gap, systemic challenges such as rigid curricula, a lack of awareness and commitment, resource constraints and limited stakeholders’ support persisted. A combined approach to accounting ethics education is required to foster ethical behavior. Hence, the structuration theory highlighted how meanings, power dynamics and norms and values constrained and enabled the provision of accounting ethics education.

The study emphasized the need for stakeholders to influence policymakers’ perceptions regarding the significance of accounting ethics education. The study also provided empirical support for structuration theory. Future research should involve cross-cultural comparisons and explore the ethical behavior of accounting professionals using diverse research designs to highlight the gaps and inform effective educational interventions.

This study contributes to the theoretical application of structuration theory in accounting ethics education. It underscores the critical role of comprehensive curricular reforms, stakeholder engagement and resource allocation.

Driven by the anticipated global stagflation, this straightforward yet novel study examines the cost of inflation as a macroeconomic factor by investigating its influence on stock market growth. Thus, this paper aims to examine the impact of inflation on the probability of initial public offering (IPO) withdrawal decision.

The paper employs a large dataset that covers the period January 1995–December 2019 and comprises 33,536 successful or withdrawn IPOs from 22 nations with various legal and cultural systems. This study applies a probit model utilizing version 15 of Stata statistical software.

This study finds that inflation is substantially and positively correlated with the likelihood of IPO withdrawal. Results of this study show that the IPO withdrawal decision increases up to 90% when the inflation rate climbs by 10%. Multiple robustness tests provide consistent findings.

This study's implications are important for researchers, investment banks, underwriters, issuers, regulators and stock exchanges. When processing IPO proposals, investment banks, underwriters and issuers must consider inflation projections to avoid negative effects, as demonstrated by the findings. In addition, regulators and stock exchanges must be aware of the detrimental impact of inflation on competitiveness in attracting new listings.

To the best of the authors’ knowledge, this study is the first to present convincing evidence of a major relationship between IPO withdrawal decision and inflation.

The purpose of this paper is to examine the importance of contractual management (CM), process management (PM) and human management (HM) factors as critical success factors (CSFs) in Malaysian housing projects. Additionally, it delves into the moderating influence of knowledge sharing (KS) on the relationship between HM and project success.

This study used a survey-based instrument to collect data from a total of 133 G7 class contractors. The stratified sampling method was used for data collection. Subsequently, structural equation modeling with SmartPLS was used for model evaluation.

The findings of this study indicate that CM, PM and HM exhibit significant relationships with housing project success. Furthermore, the research reveals that KS acts as a moderator in the relationship between HM practices and the success of housing projects.

Although this study identified a significant relationship in explaining CSFs for housing project success in Malaysia, it only considers internal CSFs such as CM, PM and HM. It is suggested that future research incorporate external factors such as political support, national policy, currency stability and industry structure to provide a more comprehensive understanding of housing project success.

The results provide supportive evidence that CM, PM and HM are important CSFs in the success of housing projects. This finding is consistent with relational contractual theory, systems theory and social interaction theory. Moreover, the research underscores the nuanced impact of KS, serving as a moderating factor in the association between HM and project success. Consequently, these outcomes substantiate the applicability of the socialization, externalization, combination and internalization framework within the construction sector, particularly within the sphere of housing sector.

This research paper examines the perspectives of marketing professionals on the impact of AI on marketing strategies.

Through interviews with 34 marketers in Singapore, the study uncovers the benefits, challenges, and potential risks associated with AI implementation.

The findings reveal improved efficiency, enhanced customer insights, and personalized marketing experiences as benefits of AI. Challenges identified include skill gaps, ethical considerations, resource constraints, and resistance to change.

The research provides important implications for marketers and companies, guiding decision-making and promoting the responsible adoption of AI in marketing strategies.

This paper aims to examine the extent to which board composition and ownership structure (OS) affect a firm’s dividend payout (DP) policy.

The sample comprises a total of 1,432 firm-year observations for Gulf Cooperation Council (GCC) nonfinancial firms. Total 179 firms were analyzed from 2009 to 2016. To test the research hypotheses, the paper used panel data analysis (i.e. fixed effects model) and instrumental variable method to ensure the robustness of results against endogeneity effects.

Corporate governance (CG) variables were found to significantly impact DP. Specifically, independent directors on board, institutional ownership and royal ownership were positively associated with DP. In contrast, board size, management, government and family ownership had a negative association with DP. The empirical evidence presented in this study supports that CG elements can be both an outcome and substitute of DP as a disciplinary mechanism.

This study excluded financial firms from the sample list. Future studies should carry out on financial firms to observe if the findings are different. Future research is suggested to incorporate more CG mechanisms. Future studies are suggested to use a dynamic panel regression due to its advantages.

Practitioners, such as investors and lenders operating in GCC markets, can derive valuable insights from this study regarding the influence of board components on DP policy. The study also provides information about the investment and business environment in GCC.

The contribution of this study lies in providing empirical evidence regarding the impact of CG mechanisms and OS on DP in GCC countries.

This study aims to examine the association between institutional investors’ ownership (IOW), politically connected firms (POCF) and audit report lag (AUDRL).

This study employs a feasible generalised least squares (FGLS) model for panel data to examine the association between IOW, POCF and AUDRL for Malaysian publicly listed companies.

The findings reveal a statistically significant negative relationship between IOW and AUDRL, with this negative relationship being more pronounced amongst POCF. Additionally, the results demonstrate that the relationship between IOW and AUDRL varies depending on the domicile of IIs (local vs. foreign). Specifically, local institutional investors exhibit a negative and statistically significant relationship with AUDRL, whilst foreign institutional investors show a positive and statistically significant relationship with AUDRL.

The results of this study provide a new understanding of auditor responses to institutional investor monitoring and political connections (PCs) in an emerging economy.

The research aims to provide empirical evidence on the relationship between financial statement comparability (FSC) and cost of equity (COE) in an emerging market.

Specifically, this study examines the relationship between FSC and COE of Vietnamese listed firms. The research uses the System Generalized Method of Moments regression techniques for a panel data set of 454 companies for the period 2015–2022.

The authors find that firms with high comparability of financial statements have lower COE. To confirm the research findings, the authors conduct the robustness test by using different proxies for the cost of equity. Consistent results are found.

The study contributes to the overall understanding of the relationship between FSC and COE, and suggests policy implications for relevant stakeholders such as managers, regulatory bodies and investors. Especially, regarding policymakers, this study could provide more insight into how the accounting convergence process impacts the effectiveness of a firm’s capital allocation.