Search results

This study aims to assess the effect of material internal control weaknesses (MICW) on the relationship between ownership structures and future-oriented disclosure.

A total number of 197 firms were assessed in this study during 2014–2021. Two measures were used for MICW. First, the number of existing MICW was assessed in independent auditors’ reports. In Iran, the maximum number of weaknesses is 13. Second, the scoring (0 or 1) method was used as a dummy variable, 1 for a firm with MICW and otherwise 0. Moreover, the scoring (0 or 1) method was used to measure the level of future-oriented disclosure of 13 indicators.

The findings showed that institutional ownership and managerial ownership have a significant positive effect on future-oriented disclosure, whereas the MICW have a significant negative effect on future-oriented disclosure. In addition, MICW played a moderator role in the relationship between ownership structures and future-oriented disclosure. The robustness checks confirmed the results.

As the studies conducted on future-oriented disclosure and the contributing factors are limited, and also the effect of MICW on future-oriented disclosure is not explored, the present findings can show the importance of the study, and fill the gap in this field. This study offers theoretical and practical implications to drive policymakers and managers to the effectiveness of internal control and future-oriented transparency.

This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement.

Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions.

The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories.

This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving.

The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Ordinary direct capitalization is normally considered procyclical in its present form (De Lisle Grissom, 2011); for this reason, an alternative approach to direct capitalization may be useful in the determination of a robust opinion of value. The valuation standards propose an alternative determination of terminal value in the discounted cash flow analysis, recommending that for cyclical assets, the terminal value should consider … “the cyclical nature of the asset and should not be performed in a way that assumes “peak” or “trough” levels of cash flows in perpetuity” (IVS 105 Valuation Approaches and Methods para 50.21 lett e).

The introduction in International Valuation Standards (IVS) of Cyclical Assets raises several questions for the community of real estate professionals and academicians (IVS, 2022, 105 Valuation Approaches and Methods para 50.09 lett d). Cyclical assets can be defined as property whose value is “influenced by upturn and downturn of the market in a significant way” (d’Amato et al., 2019).

The paper proposes different solutions to the problem. The determination of the exit value using cyclical capitalization allows for a prudent assessment of the value and may be used either as a valuation procedure or a risk analysis method.

The valuation comparison with the traditional valuation techniques will be based on an iteration of exit value in order to determine the effects of the valuation procedure on the opinion of value.

The implication of the valuation procedure is the introduction of a countercyclical valuation method to determine the exit value in order to reach stable and reliable valuations for income-producing properties.

These models may have a social implication, providing valuation for income-producing properties that may deal with the property market cycle in a more efficient way, providing efficient valuation for banks and institutions.

The paper is the first application of such a valuation procedure to the determination of exit value.

Nowadays, designing environmentally compatible buildings with acceptable performance in terms of cost, materials, and energy efficiency is considered crucial for developing sustainable cities. This research aims to identify and rank the most influential factors in the application of Building Information Modeling (BIM) systems in the smartification of green and sustainable buildings.

The present research is applied and descriptive. In this study, we identified the most influential factors in the application of Building Information Modeling (BIM) systems through library studies and expert opinions. Data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy VIKOR method was employed for analysis.

The results show that the most influential factors in the application of Building Information Modeling (BIM) systems in the Smartification of green and sustainable buildings, in order, are: “Energy saving and consumption reduction,” “Increased productivity and efficiency,” “Life-cycle assessment (LCA),” “Eco-friendly design,” “Integration with IoT and other technologies.”

In this study, while addressing the intersection of BIM technology, green building principles, and smart building objectives to optimize the performance of buildings during their life cycle, the most influential factors in the use of this system were ranked based on the criteria of “impact level,” “importance level,” and “availability of necessary tools” for implementation in Kerman. Moreover, solutions for more effectively utilizing this system in the smartification of green and intelligent buildings were proposed.

This study investigates the factors influencing the intention to use Islamic Debt Financing (IDF) among owners-managers of Micro, Small, and Medium Enterprises (MSMEs) in Morocco.

We developed and validated a comprehensive model that examines the key behavioral elements affecting attitude towards IDF, entrepreneurial achievement motivation, and IDF usage intention. Entrepreneurial achievement motivation was assessed as a mediator in the relationship between attitude towards IDF and usage intention. Gender and Islamic religiosity were examined as moderators through Multi-Group analysis. Utilizing data from 266 Moroccan MSMEs, we applied structural equation modeling for empirical evaluation.

Results revealed direct relationships with financial literacy, risk perception, and cost perception negatively affecting attitude toward IDF. Financial suitability is inversely related to attitude toward IDF. Entrepreneurial self-efficacy and subjective norms positively influenced entrepreneurial achievement motivation. Attitude toward IDF positively impacted entrepreneurial achievement motivation, and both attitude toward IDF and entrepreneurial achievement motivation were linked to the intention to use IDF. Empirical findings indicated that entrepreneurial achievement motivation partially mediates the relationship between attitude towards IDF and usage intention. Multi-group analysis revealed a significant moderating effect of Islamic religiosity on three out of nine structural relationships.

This study is primarily limited by the absence of a longitudinal examination tracking real usage behavior. Moreover, data collection focused on analyzing the behavioral intention to use Islamic debt-based financing products, excluding equity-based financing instruments. Finally, our proposed model concentrates on establishing usage intention solely based on demand-side factors without explicitly integrating supply-side consideration.

Our findings contribute to a deeper understanding of IDF adoption behavior and have the potential to support the development of more effective public policies, targeted promotional campaigns, and impactful financial education programs for MSMEs in similar markets.

This paper represents the first attempt to address MSMEs’ apprehensions regarding Islamic debt-based financing products in the MENA region.

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

The building sector is vital to a nation’s economy, as it has a major influence on economic activity and growth, job creations and the advancement of infrastructure. Intricate challenges that are inherent in crises such as the COVID-19 outbreak lead to material scarcities, project delays, labor shortages, escalated expenses, funding challenges, regulatory obstacles and dwindling investment funds, all of which culminate in costs that are in excess of those budgeted. While numerous studies have explored the ramifications of COVID-19 on project budgets, there is little, if any, data available on forecasting the magnitude of this impact.

This investigation seeks to bridge this knowledge deficiency by devising a predictive tool grounded in an ordinal logistic regression method. An online survey was designed and disseminated to gauge the views of construction field experts about the diverse contributors to excessive costs during the viral outbreak, and a predictive tool, crafted from the survey participants’ feedback.

Findings showed that smaller-scale enterprises and contractor-centric establishments faced greater adversities than medium-to-large ones and consultancy-or-owner-type entities.

The insights from this research shed light on the amplified risk of higher project costs amid health crises or analogous events, underlining the imperative need for fortified risk management approaches to bolster project outcomes. By factoring in demographics, this research offers policymakers a refined lens through which to customize interventions and promote balanced and enduring advancement in the construction industry.

The process of predicting construction costs and forecasting price fluctuations is a significant and challenging undertaking for project managers. This study aims to develop a construction cost index (CCI) for Jordan’s construction industry using fuzzy analytic hierarchy process (FAHP) and predict future CCI values using traditional and machine learning (ML) techniques.

The most influential cost items were selected by conducting a literature review and confirmatory expert interviews. The cost items’ weights were calculated using FAHP to develop the CCI formula.

The results showed that the random forest model had the lowest mean absolute percentage error (MAPE) of 1.09%, followed by Extreme Gradient Boosting and K-nearest neighbours with MAPEs of 1.41% and 1.46%, respectively.

The novelty of this study lies within the use of FAHP to address the ambiguity of the impact of various cost items on CCI. The developed CCI equation and ML models are expected to significantly benefit construction managers, investors and policymakers in making informed decisions by enhancing their understanding of cost trends in the construction industry.

In 2000, Cañibano et al. published a literature review entitled “Accounting for Intangibles: A Literature Review”. This paper revisits the conclusions drawn in that paper. We also discuss the intervening developments in scholarly research, standard setting and practice over the past 20+ years to outline the future challenges for research into accounting for intangibles.

We conducted a literature review to identify past developments and link the findings to current accounting standard-setting developments to inform our view of the future.

Current intangibles accounting practices are conservative and unlikely to change. Accounting standard setters are more interested in how companies report and disclose the value of intangibles rather than changing how they are determined. Standard setters are also interested in accounting for new forms of digital assets and reporting economic, social, governance and sustainability issues and how these link to financial outcomes. The IFRS has released complementary sustainability accounting standards for disclosing value creation in response to the latter. Therefore, the topic of intangibles stretches beyond merely how intangibles create value but how they are also part of a firm’s overall risk and value creation profile.

There is much room academically, practically, and from a social perspective to influence the future of accounting for intangibles. Accounting standard setters and alternative standards, such as the Global Reporting Initiative (GRI) and European Union non-financial and sustainability reporting directives, are competing complementary initiatives.

Our results reveal a window of opportunity for accounting scholars to research and influence how intangibles and other non-financial and sustainability accounting will progress based on current developments.

This study aims to investigate the opacity of bank assets because of the International Financial Reporting Standard (IFRS) 9 implementation. It highlights that the Asian-Pacific countries’ banking industries are experiencing economic volatility. In other words, it examines information asymmetries because of the standards requiring a mechanistic treatment. Thus, this focuses on the tragedy of the commons (ToTC) caused by the implementation of the standard.

This research selects a sample of banking firms in the Asia-Pacific region from 2010 to 2021. Furthermore, it examines the impacts of IFRS 9’s implementation on earnings forecasts and share-return conveyances. This research first uses the OLS regression for examining the bank assets’ opacities, which may affect future earnings and information conveyancing. Second, it arranges these opacities, earnings and stock returns with the 2-SLS regression to find the staging associations because of hierarchical relevances.

This study finds that bank assets’ opacity is caused by a standard’s implementation, which is a ToTC, and this study signifies its first occurrence. Simultaneously, it recognises an information asymmetry because of the implemented procedural calculation mandated by the standard. Furthermore, these opacities affect future earnings and information conveyancing that inherited information asymmetries, which have affected them as the second ToTC. Finally, current and future earnings as a consequent impact of asset opacity are recursively associated with stock return conveyancing as the third ToTC.

This study demonstrates hierarchical information about bank asset opacities, starting by recognising and measuring them in financial statements. Then, these recognised and measured asset opacities are associated with current and future earnings, ending on the ordinarily and staged influencing of stock return conveyancing. Moreover, it reveals hierarchical information in the direct-ordinarily and staged associations among bank asset opacities, earnings and return conveyances. Thus, these associations are valid and occur because of the mandates of the standard’s measurement.