Search results

Financial statement fraud has become a global concern, and auditors are increasingly focused on identifying and investigating it. Auditors may play a crucial role in investigating and reducing financial statement fraud, and this is particularly important in developing countries where fraudulent practices are more prevalent due to the lack of strict regulations and oversight. This study investigates whether enhanced audit quality has an impact on reducing financial statement fraud. The primary aim is to recognize whether a higher level of audit quality relates with a decrease in fraudulent activities in Indonesia, which is one such country that has not yet adopted IFRS.

This study investigates the effect of audit quality, as measured by audit tenure, audit fee, and audit size, on the dependent variable of financial statement fraud, as indicated by Dechow F-value. The sample for this study comprises 951 observations from 2015 to 2020, and the research design utilizes a panel data approach. To test the main hypothesis, OLS, and GMM estimation techniques are employed.

The analyses reveal a negative relationship between audit tenure and financial statement fraud. This suggests that shorter audit tenure may be associated with an increased risk of financial statement fraud. This heightened risk could stem from auditors having limited time to thoroughly understand the company's operations and internal controls, potentially making it more challenging to detect and prevent fraudulent activities perpetrated by the client. Conversely, a positive relationship is identified between audit fees and financial statement fraud, suggesting that companies paying higher fees may be engaging auditors less adept at detecting fraudulent activities. Furthermore, a negative relationship is observed between Big-5 and financial statement fraud, which may be due to the greater resources, expertise, quality control, scrutiny, reputation, and ethical conduct of Big-5 audit companies.

This study only focused on listed companies in Indonesia, therefore, caution should be exercised when generalizing the findings to other developing and Muslim countries such as Malaysia. The findings may differ due to the adoption of IFRS in Malaysia. As such, it is important for future studies to include Malaysia as a sample and compare the results with those of Indonesia. This comparison would demonstrate the impact of IFRS adoption on the relationship between audit quality and financial statement fraud and provide insights for policy makers in Indonesia.

The findings of this study have important implications for developing countries that have been shown to be more susceptible to fraud than developed countries. This study contributes to the existing research on the role of audit quality in reducing financial statement fraud and emphasizes the need for auditors and accountants to take a proactive approach in detecting and investigating financial fraud.

This study is a new study because it investigates the relationship between audit quality and financial statement fraud in Indonesia, a developing Muslim country that has not yet adopted International Financial Reporting Standards (IFRS). The study provides valuable evidence on the unique factors that influence fraud in Indonesia and fills a gap in the literature as previous studies on this topic have largely focused on developed countries. Additionally, the study recommends that policymakers in Indonesia consider implementing IFRS to improve the reliability of financial reporting and strengthen the effectiveness of the auditing process, thus reducing the incidence of fraud.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

This paper aims to model and analyze Jeffery Hamel’s channel flow with the magnetohydrodynamics second-grade hybrid nanofluid. Considering the importance of studying the velocity slip and temperature jump in the boundary conditions of the flow, which leads to results close to reality, this paper intends to analyze the mentioned topic in the convergent and divergent channels that have significant applications.

The examination is conducted on a EG-H_2 O <30%–70%> base fluid that contains hybrid nanoparticles (i.e. SWCNT-MWCNT). To ensure comprehensive results, this study also considers the effects of thermal radiation, thermal sink/source, rotating convergent-divergent channels and magnetic fields. Initially, the governing equations are formulated in cylindrical coordinates and then simplified to ordinary differential equations through appropriate transformations. These equations are solved using the Explicit Runge–Kutta numerical method, and the results are compared with previous studies for validation.

After the validation, the effect of the governing parameters on the temperature and velocity of the second-grade hybrid nanofluid has been investigated by means of various and comprehensive contours. In the following, the issue of entropy generation and its related graphical results for this problem is presented. The mentioned contours and graphs accurately display the influence of problem parameters, including velocity slip and temperature jump. Besides, when thermal radiation is introduced (Rd = +0.1 and Rd = +0.2), entropy generation in convergent-divergent channels decreases by 7% and 14%, respectively, compared to conditions without thermal radiation (Rd = 0). Conversely, increasing the thermal sink/source from 0 to 4 leads to an 8% increase in entropy generation at Q = 2 and a 17% increase at Q = 4 in both types of channels. The details of the analysis of contours and the entropy generation results are fully mentioned in the body of the paper.

There are many studies on convergent and divergent channels, but this study comprehensively investigates the effects of velocity slip and temperature jump and certainly, this geometry with the specifications presented in this paper has not been explored before. Among the other distinctive features of this paper compared to previous works, the authors can mention the presentation of velocity and temperature results in the form of contours, which makes the physical analysis of the problem simpler.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

This study conducts a thorough literature review and meta-analysis to explore the adoption of blockchain technology (BCT) in supply chain management (SCM). It aims to identify the potential benefits, challenges, and critical factors influencing the implementation of this technology in supply chains.

A systematic analysis of 157 highly cited publications is performed, offering insights into research trends, citations, industries, research methods, and contextual aspects. Thematic analysis is employed to uncover key findings regarding enablers, barriers, drivers, challenges, benefits, and drawbacks associated with BCT adoption in supply chains.

The analysis highlights the complexities and opportunities involved in adopting BCT in SCM. A proposed model aligns with five dimensions, including inter-organizational, intra-organizational, technological, legal, and to assist businesses in harnessing the potential of BCT, overcoming obstacles, and managing challenges. This model provides practical recommendations for navigating the intricacies of BCT implementation while balancing associated challenges and risks.

Organizations operating in supply chains can leverage the insights gained from this investigation to position themselves at the forefront of BCT adoption. By implementing the proposed model, they can unlock benefits such as increased transparency, efficiency, trust, and cost reduction.

The novelty of this paper lies in its extensive review of publications on Blockchain Technology adoption in supply chains. It offers insights into various aspects such as enablers, barriers, drivers, challenges, benefits, and drawbacks. Additionally, the paper presents a comprehensive model specifically designed for successful adoption of Blockchain Technology in supply chains. This model addresses multiple dimensions including inter-organizational, intra-organizational, technological, legal, and financial.

The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.

Green entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.

The study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.

Results indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.

The study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.

This study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

The use of polysaccharides increases solubility and consistency and causes functions such as viscosity? Moisture and food emulsifier stabilizer. This study aims to enrich the formulation of low-fat mozzarella cheese using microcoated vitamin D3 (VD3).

This study investigates the addition of hydrocolloids to low-fat mozzarella cheese to enhance its properties and nutritional value. Tests were conducted on cheese samples with 0.05% and 0.25% hydrocolloid concentrations at various stages: before production and at three and six months’ postproduction. The samples were evaluated for elasticity, pH and solubility to select the best one, which was then fortified with VD3. The vitamin was microencapsulated using alginate and whey protein to shield it from light and oxygen, optimizing the formula using the response surface method. The fortified cheese was tested for VD3 content over its shelf life.

Results indicated that all hydrocolloids tested improved moisture and meltability of the cheese while higher protein levels increased stretchability two to threefold. Rice starch hydrocolloid at 0.05% concentration was chosen due to superior sensory scores and minimal oil separation. This study concluded that VD3 levels remained stable during the cheese’s shelf life, suggesting that this approach could enhance the nutritional value of low-fat cheese without compromising its quality. Therefore, after examining the obtained results and comparing the regression models, the results indicated that the Quadratic model was chosen to investigate the effect of independent variables on the response rate, which had a statistically significant difference with other models (p = 0.0019). Also the results of the area under the curve and using the encapsulation efficiency equation, the percentage of microencapsulated vitamin was obtained, and according to the simulation results, the encapsulation efficiency was reported as 89.02%.

Developing innovative functional dairy products fortified with VD3 could improve the vitamin D status in deficient populations. Therefore, these designs can be applied at industrial scales for functional cheese production.

The present study aims to examine and explain cultural heritage destination by applying multiple dimension image model (cognitive–affective–conative aspects).

A total of 233 international heritage-based tourists were surveyed on-site at some of the most prominent historical attractions in Malacca, one of the first cities in Malaysia declared as a UNESCO World Heritage Site. The covariance-based structural equation modelling was applied to examine the hypotheses.

The structural equation modelling validated that cognitive image aspects such as living culture, intangible and tangible are affected positively. The effects are statistically significant for conative and affective images. On the other hand, cognitive tangible image is an aspect that impacted affective image to a lesser degree than conative image. Whereas affective image attributes were found to have significant and positive influence on conative image.

This study enriches the limited empirical research study on heritage image conceptualisation by expanding into tri-component model. The destination image has garnered a great deal of attention, particularly due to its significant and impactful influence on the decision-making and the sustainable behaviour of tourists, and it has since become the subject of many studies in the tourism and hospitality literatures. But most research concerning heritage image for destinations has considered the construct uni-dimensionally.