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The purpose of this study is to investigate a joint economic lot-size model with the possibility of cofinancing between members of a three-echelon supply chain (SC) including one supplier, one manufacture and one retailer. Given the differences in credit as well as differences in access to capital markets, SC members will be able to create a financial alliance to maximize the profits of each member. This study proposed a model to maximize the annuity stream of the SC by considering the financial interaction between SC members.

This joint economic lot-sizing problem was described and modeled mathematically. To evaluate the mathematical model, different scenarios were considered with (and without) the possibility of financial interaction.

It is suggested that, in addition to the goods and information flow among SC members, proper financial flow can also have an impact on the improvement of SC performance.

While previous studies consider cofinancing between members of a two-echelon SC, this paper considers a three-echelon SC including one supplier, one manufacturer and one retailer where financial cooperation between different levels of the SC in both upstream and reverse directions will be possible.

This paper evaluates the factors influencing the utilization of the Internet of Things (IoT) for sustainable facilities management (SFM) practices in Saudi Arabia.

A mixed approach, combining a literature review, pilot-testing and questionnaire survey, was adopted to evaluate the factors. Twenty-seven factors were identified and grouped into four groups: technical, business and organizational, operational and security and privacy. The questionnaire was distributed to 30 facilities managers and 30 IoT specialists, totaling 60 practitioners, to determine the effect index of each factor. The practitioners' consensus on the ranking of the factors was then determined.

The study identifies the top-ranking factors as: “Difficulty in ensuring data security and protection,” “Difficulty in ensuring data privacy and confidentiality” and “Limited awareness and understanding of IoT benefits and capabilities.” These factors highlight the challenges to successful IoT implementation in the FM sector. The FM sector could benefit from utilizing IoT while maintaining the security, privacy and effectiveness of building operations by successfully addressing these concerns. A high level of consensus on the ranking of the factors was observed between facilities managers and IoT specialists. This was substantiated by a Spearman’s rank correlation coefficient of 0.79.

This study enriches the literature by combining practical insights from facilities managers with technical expertise from IoT specialists on the factors impacting IoT implementation in the Saudi Arabian FM sector. Beyond academic contributions, it provides practical insights for industry professionals, fostering a culture of knowledge-sharing and guiding future research in this field.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

This study aims to investigate the effect of electrodeposition parameters (i.e. time and voltage) on the properties of hydroxyapatite (HA) coating fabricated on Ti6Al4V surface.

A full factorial design along with response surface methodology was utilized to evaluate the main effect of independent variables and their relative interactions on response variables. The effect of electrodeposition voltage and deposition time on HA coatings Ca/P molar ratio and the size of deposited HA crystals were examined by structural equation modeling (SEM). The formation of plate-like and needle-like HA crystals was observed for all experiments.

The results obtained showed that the higher electrodeposition voltage leads to lower Ca/P values for HA coatings. This is more significant at lower deposition times, where at a 20-minute deposition time, the voltage increased from 2 to 3 V and the Ca/P decreased from 2.27 to 1.52. Full factorial design results showed that electrodeposition voltage has a more significant effect on the size of the deposited HA crystal. With increasing the voltage from 2 to 3 V at a deposition time of 20 min, the HA crystal size varied from 99 to 36 µm.

The investigation delved into the impact of two critical parameters, deposition time and voltage, within the electrodeposition process on two paramount properties of HA coatings. Analyzing the alterations in coating characteristics relative to variations in these process parameters can serve as a foundational guide for subsequent research in the domain of calcium-phosphate deposition for implants.

Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices.

The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result.

The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Building Index certification. The empirical evidence from this study suggests that green certification significantly affects the sales price of residential properties in Malaysia. The findings of this research will help investors identify measurable factors that affect the transaction prices of green-certified residential real estate. These identifications will facilitate the development of strategic plans aimed at achieving sustainable rates of return in the sustainable residential real estate market.

Specifically, this research will contribute to achieving area 4 of the 11th Malaysia Plan, which pertains to pursuing green growth for sustainability and resilience. This will be achieved by enhancing awareness among investors and homebuyers regarding the importance of green residential buildings in contributing to the environment, the economy and society.

The regression model for housing attributes and green certification on house price developed in this study could offer valuable benefits to support and advance Malaysia in realising its medium and long-term goals for green technology.

The direct nexus between board characteristics, earnings management (EM) practices and dividend payout is examined in this study, followed by an examination of the indirect mediation impact of EM practices in the nexus between board characteristics and dividend payout. It aims to provide new empirical evidence from the Jordanian market, which is an emerging market.

The study population consists of all service firms that were listed on the Amman Stock Exchange (ASE) between 2012 and 2019. Due to the lack of availability of their complete data during the period, four service firms were omitted from the population; hence, a sample of 43 service firms was acquired over the time frame (2012–2019), yielding a total of 344 firm-year observations. Moreover, panel data analysis was employed in this study, and data for the study were acquired from yearly reports as well as the ASE's database.

Based on the GMM estimator findings, board size and independence have a negative and significant influence on the EM, but CEO/chairman duality has a positive and significant impact. Simultaneously, the impacts of female representation on the board of directors and the number of board meetings were both positive but insignificant. The findings also found that four board characteristics, including board size, female representation on the board of directors, CEO/chairman duality and the number of board meetings, had a significant negative or positive effect on dividend payout, while board independence did not. Additional findings show that EM practices have a direct negative insignificant effect on dividend payout, whereas EM practices partially mediate the relationship between board characteristics and dividend payout.

The current study's limitation is that it only searched in Jordanian service firms listed on ASE from 2012 to 2019 to fulfill the study's objectives; thus, we urge that future work explores the study models for other sectors, whether in Jordan or other growing markets such as the Middle East and North Africa.

The findings of this study may be utilized by analysts, investors and other strategic decision-makers to enhance Jordan's financial market's efficiency and efficacy. These findings will improve policymakers' willingness to impose appropriate constraints, perhaps boosting Jordan's financial market performance and efficacy. These findings may also help investors make more enlightened judgments by utilizing board characteristics and EM factors that predict firm dividend policy.

Contradictions in the results of earlier investigations inspired the current study, with the findings filling a gap in the existing literature. This study differs from previous studies by constructing a novel research model and analyzing the mediating influence of EM in the nexus between board characteristics and dividend payout.

Tourists' environmentally responsible behaviour (ERB) is very important for the environmental sustainability of popular tourist destinations. The main purpose of this research paper is to explore the relationship between environmental citizenship behaviour (ECB) and trust in local government with ERB.

A theoretical model was proposed and tested. Data were collected from domestic tourists (n = 555) visiting Antalya, on the Mediterranean coast of Turkey.

The results show that all dimensions of ECB (helping, engagement and initiatives) have a positive effect on the formation of ERB among domestic tourists. The results also show that trust in the local government of the destination has a moderating effect on the relationship between the two dimensions of ECB (engagement and initiatives) and ERB. The helping dimension of ECB has the highest positive impact factor on ERB. The results provide a comprehensive framework for further research. The proposed model is strongly supported by the findings.

Local policy makers can focus on how to increase the environmental motivation of domestic tourists. Environmentally friendly projects can be developed to attract their attention. This will ensure that they take more responsibility for the environment at the destination.

Understanding the potential impact of local government on tourist behaviour is crucial for sustainability. However, there are few studies that directly examine the moderating effect of trust in local government on the relationship between ECB and ERB. This study addresses this gap and makes novel contributions to the relevant literature.

Financial fraud is a severe corporate fraud committed for achieving various objectives, like attaining financial targets, lowering debt and providing good signals to the market. Such financial fraud deceives stakeholders and results in substantial financial losses. This study aims to detect financial fraud using the modified Beneish M-Score, the most appropriate forensic tool for fraud detection. Furthermore, the current study also examines the influential role of the fraud triangle’s elements (pressure, opportunity and rationalization) on financial fraud in nonfinancial firms during 2018–2021, offering insight for understanding and mitigating fraudulent activities in the corporate world.

Financial fraud is treated as a dependent variable measured through a modified Beneish M-score, while the fraud triangle elements (pressure, opportunity and rationalization) are measured through six proxies, which are financial stability, leverage, financial target, nature of the industry, the effectiveness of supervision and auditor changes.

The study's finding proclaimed that fraud triangle elements result in financial fraud. Findings unveil that all elements (pressure, opportunity and rationalization) of the fraud triangle significantly influence financial fraud. The study confirms that these elements must be considered to protect investors and provide a safe environment for investment.

Rare literature found addressing the detection of financial fraud and its nexus with the fraud triangle specifically in Pakistan where deficient governance is notably prevalent. This study attempts to fill such a gap and contribute to knowledge.

This paper aims to examine the consequences of board diversity (BD) in Kuwait. In particular, it examines the impact of BD (gender, age and nationality) on earnings management (EM).

The research uses data from 103 non-financial Kuwaiti-listed companies from 2010 to 2017. The data is collected from the companies’ data from secondary sources such as their annual reports. The data analysis methods are correlation, multi-regression and robust regression. EM is measured using the modified Jones model (1995) and Kothari et al. (2005).

The findings show a negative association between gender diversity (GD) and EM. It also found a positive relationship between age diversity (AD) and EM and no relationship between national diversity (ND) and EM.

This study’s results have significant implications for investors. The practical empirical findings indicate that GD on the board did not impact on EM. Also, it is more important to have senior directors on the board than AD to reduce EM. There is no need to employ any foreigners because they do not affect EM.

It contributes to the growing body of literature on BD by investigating its effect on EM. Furthermore, building on the broader literature on gender, age and ND by highlighting the critical role that women, young people and foreign directors play in improving boards' monitoring role on EM. More specifically, it contributes to existing knowledge, provides a theoretical contribution and makes a methodological contribution.

This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.

This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.

This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.