Working capital management is an important issue in financial management, considering its large role on the profitability and liquidity of firms. This paper aims to explore the effects of working capital management on profitability in 14 Saudi retail companies that are listed in the Saudi Stock Exchange (Tadawul) over the period of 2011-2014. This study will employ panel data regression analysis using Pooled OLS to test the relationship between working capital components, which are the cash conversion cycle (CCC), current assets to total assets (CATAR), current assets to current liabilities ratio (CACLR), current liabilities to total assets ratio (CLTAR) and debt to total assets ratio (DTAR) and profitability measured by return on assets (ROA) and return on invested capital (ROIC). The working capital management components represent the independent variables, while the profitability variables represent the dependent variables used in the model. The results show no significant relationship between working capital management components and ROA and ROIC in all the companies and the ones who predominantly deal with services, except for companies that deal with mainly goods, where there is a significant negative relationship between CACLR and ROA.

This article investigates the effect of financial technology (FinTech) growth on bank performance in the Gulf Cooperation Council (GCC) region. The application is conducted on a panel dataset containing annual observations of banks from 2012 to 2021 using the generalized method of moments (GMM) method. FinTech growth is set as an explanatory variable on three proxies of bank performance, namely, return on assets (ROA), return on equity (ROE), and net interest margin (NIM). Moreover, several control variables are added to the model, including bank-specific and macroeconomic variables. The results are significant as all the proxies of bank performance are negatively affected by the growth of FinTech startups. Consequently, banks are urged to proactively invest in FinTech startups and engage in partnerships to avoid the risk of disruption.

Graph-based models have emerged as a promising approach for capturing intricate relationships in various real-world applications [1], including recommendation systems. A novel approach called P-GNN (Partitioned-Graph Neural Network) has been proposed in this work, which utilizes the bipartite graph structure commonly used in recommendation systems. The approach generates separate embeddings for users and items, allowing highly discriminative recommendations. A significant advantage of this approach is its ability to address the oversmoothing and cold-start problem by computing separate subgraphs for users and items. This feature enables the generation of embeddings even for new users or items. The effectiveness of the proposed approach has been evaluated on multiple benchmark datasets and compared against state-of-the-art methods. The results demonstrate that Partitioned-GNN outperforms existing approaches, highlighting the potential of graph-based methods for recommendation systems.

The potential of both current and future IoT applications is substantial in improving user comfort, productivity, and automation. However, to continue implementing this technology on a larger scale and across various devices, it is necessary to have high levels of security, privacy, authentication, and resilience against attacks. With the development of many IoT technologies that rely on the internet, there is a constant threat of cyberattacks that can wreak havoc globally. Denial of service attacks can disrupt network security and result in illegal access, misuse, or alteration. Using IoT in real-world environments requires security and privacy considerations. Increasing cyberattacks and security breaches have made safeguarding IoT infrastructure and networks a top priority in today’s computing landscape. This paper examines the latest solution for IoT security using Intrusion detection systems (IDSs) to achieve secure end-to-end environments. This paper explores the profound impact of IDSs on IoT security such as vulnerability detection, Anomaly detection, real-time alerts, incidence response, etc. Furthermore, the paper focuses on various challenges regarding the applicability and implementation of IDS technology in real-world scenarios.

This article investigates the effect of financial technology (FinTech) growth on bank performance in the Gulf Cooperation Council (GCC) region. The application is conducted on a panel dataset containing annual observations of banks from 2012 to 2021 using the generalized method of moments (GMM) method. FinTech growth is set as an explanatory variable on three proxies of bank performance, namely, return on assets (ROA), return on equity (ROE), and net interest margin (NIM). Moreover, several control variables are added to the model, including bank-specific and macroeconomic variables. The results are significant as all the proxies of bank performance are negatively affected by the growth of FinTech startups. Consequently, banks are urged to proactively invest in FinTech startups and engage in partnerships to avoid the risk of disruption.

There are six participation banks in Turkey, and they are expected to contribute significantly to the economy in the future. The aim of this study is to assess the efficiency of these participation banks based on financial data from 2010 to 2020. Data envelopment analysis (DEA), a non-parametric efficiency approach, is used in this study with three input variables (profit share expenses, personnel expenses, and total funds), and two output variables (profit share income and net profit). While Kuveyt Türk, Ziraat Katılım, and Türkiye Finans are found to be the most efficient, Albaraka Türk and Vakıf Katılım are inefficient – at least when the most recent observations (2018-2020) and the four-year moving average efficiency score for both outputs and inputs are used. As for the overall average scores, Türkiye Finans is the most efficient participation bank, followed by Ziraat Katılım and Vakıf Katılım. The least efficient participation banks are Kuveyt Türk and Albaraka Türk. For participation banks to gain a larger share of the market and to continue contributing to overall economic growth, policymakers must pay attention to these underperforming banks.

Energy, being a significant input in the production process is considered an important element in the socio- economic development of a country. In this context, global economy is concentrating on attaining financial stability through increased energy efficiency and plummeting the energy intensity. Efficient use of energy enables the countries to improve their trade balance whereas it helps to reduce operating costs at micro level. Using energy-efficient techniques can reduce the environmental risks posed by the higher level of economic activity. Similarly, development of financial sector stimulates economic efficiency through the expansion in the financial activities (Sadorsky, 2010). Financial development helps to reduce cost of borrowing and financial risk, thereby creating transparency between lenders and borrowers. In addition, financial sector development facilitates the acquisition of sophisticated energy efficiency products and technology. However, with the rapid economic development, the environmental degradation has become a focus point for the nations across the globe. The feedback effects from environment to economic growth have stimulated the researchers to investigate the reasons for environmental dilapidation and determine the solutions for environmental conservation. Many of these research studies are encircled around the environmental Kuznets curve (EKC) hypothesis (Grossman &Krueger, 1991) which elaborates the linkages between the environmental degradation and economic growth. Middle Eastern economies are heavily reliant on the energy-based revenues and any curb on the energy production directly affects their economic growth in the region. Furthermore, financial system in the Middle Eastern region is technologically advanced with strong regulatory frameworks related to data protection, consumer protection, cybersecurity, and anti-money laundering. These regional characteristics provide a strong base to assess the existence of environmental Kuznets curve in the region and to examine the nexus between the financial sector development, economic growth, energy efficiency and Co2 emission in the region. The relationship between the environmental quality and economic growth is explored by various studies such as Jaeger et al. (1995), Tucker (1995), Barbier (1997), Horvath (1997), Ansuategi et al. (1998), List and Gallet (1999), Stern and Common (2001), Roca (2003), Dinda and Coondoo (2006), Coondoo and Dinda (2008) and Akbostanci et al. (2009). However, this research is unique as it is first to examine the environmental Kuznets curve hypothesis for the oil based Middle Eastern economies while using a conditionally homogenous autoregressive model for the panel of Middle Eastern economies. This model considers homogeneity across the cross-sectional units with identical structural characteristics. The panel conditionally homogenous vector autoregressive specification permits the heterogeneity in the dynamic panel data set and evaluates the relationship between the observed heterogeneity across the units and their structural characteristics

Globally, housing affordability is becoming an increasing concern, particularly to the low and middle-income group. Housing priceshavebeen steadily growing over theyears, therebymake it even more burdensome,particularly for fresh graduates and low-income earners (B40),to own a home. The constant price increaseis mainly due to inflation in the property sector brought about by the very nature of the modern monetary system, particularly the fractional reserve banking andcompound interest charges, leading to housing and property bubbles. Accordingly, governments have generally failed in successfully solving the housing problem. In this regard, this paper provides an affordable housing solution through solar energy harvesting combined with Islamic financing provisions for the case of Malaysia. In our proposed model, housing can be provided in a very affordable manner regardless of the credit-worthiness of buyers with the possibilityof free-energy at the end of the tenure.

This study analyzes financial literacy in Bosnia and Herzegovina by considering three areas: interest, inflation, and diversification, with financial literacy as a multi-dimensional construct consisting of financial knowledge and financial skills. Using a cross-sectional questionnaire-based survey, 638 valid responses were collected from working-age individuals (18-65 years old). Financial knowledge and skills were analyzed through a prism of several demographic factors, including age, education, household income, and gender. Welch's F tests, ANOVA with Brown-Forsythe, LSD post hoc tests, and Welch's t-tests were performed to test the hypotheses. The findings provide evidence of moderate financial literacy. Similarly to previous studies, financial knowledge and skills partially depend on the respondent's age, education, household income, and gender. The study contributes to the current literature by taking a much-needed non-functional approach to examining financial literacy, focusing not only on financial knowledge but also on often neglected financial skills and providing insight into the unique context of Bosnia and Herzegovina.