A vibrant banking sector remains instrumental to the stability of every economy. Islamic banks are now considered as iris spuria of the banking industry. Countries such as Saudi Arabia have been hailed as the Islamic banking basin. Nevertheless, how well is this sector growing and performing in Saudi Arabia itself? This motivates us to carry out this study. The current study's key objective was to measure Sharia-compliant banks' efficiency on the CAMEL Framework, a commonly accepted framework for banks' financial health. CAMEL is fundamentally an acronym for which the first letter from the five primary segments of a bank operation is jumbled, i.e. “|C|apital adequacy, |A|sset quality, |M|anagement quality, |E|arnings ability and |L|iquidity”. The system is popularly being used for determining the financial soundness and stability of banks. The current study employs this framework to judge the financial performance of four fully Sharia compliant banks or Islamic banks in Saudi Arabia. The publicly accessible audited data of these banks over ten years was taken for analysis. From the final results of the analysis, it is found that all the banks performed stupendously well on the CAMEL framework. AlRajhi Bank was rated number one of all four Sharia-compliant banks. However other three banks namely Alinma Bank, AlBilad Bank, and Aljazeera bank have also done well and overachieved all the criterion of CAMEL's ranking. However, the study proposes a comparison of Sharia-compliant banks with conventional commercial banks. Moreover, it recommended that more banks should engage in offerings of Sharia based products.

This study aims to contribute to the ongoing debate over accounting conservatism through examining whether the corporate strategy can provide a logical justification for the conservatism, and whether this justification is consistent with or contradicts the contractual justification. In this context, the study examines the impact of corporate strategy on the level of conservatism in financial statements, and the impact of the level of conservatism on corporate investment level. Using data from a sample of non-financial Saudi listed companies, the results show that prospector companies are more likely to provide more conservative financial statements, and the level of conservatism influences investment level. The results also show that the integration between corporate strategy and the level of conservatism influence the level of investment. These results suggest that accounting conservatism can be explained in the context of both business strategy and contractual benefits. Also, the results imply that adopting business strategy could influence the financial reporting process.

Liquidity refers to the paying ability of the business organization while profitability assesses the profit earning capacity of the business organization. The liquidity of the business organization can be bifurcated into two based on time i.e., short-term and long-term liquidity. The short-term liquidity reveals the operational efficiency while long-term liquidity refers to the financial capability to repay the long-term debts of the business organization. The short-term paying ability is the management of the working capital or efficient management of the current assets and current liabilities. The current assets and current liabilities are directly related to the revenue of the business and further affected by the profitability, indirectly. The long-term paying ability or financial health of the business organization is reflected by the debts and equity ratio. The energy sector of Saudi Arabia is a prominent sector and contributes to the economy progressively. The study is based on secondary data and reveals the long-term and short-term liquidity variations and the cohesiveness of long-term and short-term liquidity with the revenue and profitability of energy sector companies. The study reveals the significant variations in the short-term and long-term liquidity and cohesiveness between the revenue, profitability, and short-term and long-term liquidity of the energy sector companies.

The aim of this study is to empirically examine the association between audit efficiency and board activity among listed energy companies in Saudi Stock Exchange (Tadawul) for the period 2012-2019. The final sample of this study consists of 32 firm-year observations. Using the Pooled OLS regression, the study finds that audit efficiency was positively related to board activity. Moreover, the result indicates the complementary role of monitoring mechanisms in which audit efficiency complements the board activity. The study also provides insightful evidence to policy makers on the link of audit efficiency and board activity among energy companies.

The focus of this study is to examine the impact of board of directors’ size and meetings on the audit committee meeting frequency among energy, materials and food and beverages sectors listed on Saudi Stock Exchange (Tadawul) for the period 2015-2017. The final sample of this study consists of 198 firm-year observations. Using the Pooled OLS regression, this study finds that board meeting is positively associated with audit committee meeting frequency. Furthermore, this study fails to report an association between board size and audit committee meeting frequency. This study provides insightful evidence to policy makers on the determinants influencing the audit committee meeting frequency among manufacturing companies.

The goal of this study is to evaluate the way digital marketing (DM) works in increasing customer satisfaction (CS) and brand loyalty (BL) at the Saudi Arabian Restaurants. The study uses 7 variables for analysis such as Service quality satisfaction (SQS) Digital engagement satisfaction (DES) Recommendation Likelihood (RL) Digital Promotions (DP) Online Presence Perception (OPP) Promotions Effectiveness (PE) Social Media Engagement (SME). Data from customers using digital media has been gathered through questionnaires. 410 respondents provided the data, which was then examined using SPSS and AMOS. The study will give management the knowledge they need to modify procedures and train employees in order to satisfy customers and promote BL. Future research can be done across several corporate sectors and cultural contexts. The premise for this study is provided by this paper, which also offers managers useful guidance regarding how to train employees to increase consumer satisfaction and BL.

IoT devices with weak security designs are a serious threat to organizations. They are the building blocks of Botnets, the platforms that launch organized attacks that are capable of shutting down an entire infrastructure. Researchers have been developing IDS solutions that can counter such threats, often by employing innovation from other disciplines like artificial intelligence and machine learning. One of the issues that may be encountered when machine learning is used is dataset purity. Since they are not captured from perfect environments, datasets may contain data that could affect the machine learning process, negatively. Algorithms already exist for such problems. Repeated Edited Nearest Neighbor (RENN), Encoding Length (Explore), and Decremental Reduction Optimization Procedure 5 (DROP5) algorithm can filter noises out of datasets. They also provide other benefits such as instance reduction which could help reduce larger Botnet datasets, without sacrificing their quality. Three datasets were chosen in this study to construct an IDS: IoTID20, N-BaIoT and MedBIoT. The filtering algorithms, RENN, Explore, and DROP5 were used on them to filter noise and reduce instances. Noise was also injected and filtered again to assess the resilience of these filters. Then feature optimizations were used to shrink the dataset features. Finally, machine learning was applied on the processed dataset and the resulting IDS was evaluated with the standard supervised learning metrics: Accuracy, Precision, Recall, Specificity, F-Score and G-Mean. Results showed that RENN and DROP5 filtering delivered excellent results. DROP5, in particular, managed to reduce the dataset substantially without sacrificing accuracy. However, when noise got injected, the DROP5 accuracy went down and could not keep up. Of the three dataset, N-BaIoT delivers the best accuracy overall across the learning techniques.

The pharmaceutical supply chain (PSC) plays a crucial role in ensuring the timely and reliable availability of essential drugs while maintaining high-quality standards. Balancing the triad of cost, time, and quality is paramount in optimizing the complexities of this supply chain. In this research, a multi-objective PSC optimization model is developed to maximize the key business factors. The dynamic nature of the PSC can significantly compromise the effectiveness of the decision making process. To deal with this challenge, a robust possibilistic flexible programming approach (RPFPA) solution methodology is proposed. This methodology provides a robust and flexible framework to tackle the uncertainties within the supply chain. To validate the proposed model and methodology, a computational analysis of a case study is conducted. The results of the analysis demonstrate the effectiveness of the model and methodology in addressing the uncertainties and complexities of the PSC. Specifically, the findings reveal that by accepting a 23.8% increase in costs, decision-makers can achieve a desirable level of robustness in their decisions. Moreover, the study identifies that the assignment of higher priority to cost objectives leads to more centralized decisions within the supply chain, while a greater emphasis on quality objectives results in a more decentralized approach. By employing the proposed approach, decision-makers can efficiently deal with the complexities and uncertainties inherent in the PSC, making well-informed choices that balance cost, time, and quality.

This research explores the influence of data mining methods on the managerial decision-making in Saudi Arabia's industrial sector, emphasizing the moderating function of cooperation. A total of 500 questionnaires were distributed to information technology managers, with 265 responses selected for data analysis. Smart PLS 4 software was used for the data analysis, and statistical measures were used to analyze the correlations between variables. The findings show that data mining approaches have a substantial positive relationship with improving decision-making and information exchange within external and internal contexts. The study also demonstrates that cooperation plays an important moderating role in these interactions, emphasizing the significance of building a cooperative atmosphere to improve the influence of data mining methods on decision-making and information sharing. The study's conclusions have practical relevance for organizations in the industrial sector. Organizations may improve their decision-making processes and information sharing by adopting data mining tools and boosting collaboration, enhancing performance and competitiveness.

This study examined the relationship between employee engagement, organizational performance, and electronic human resource management (e-HRM) in small- and medium-sized Saudi firms. In total, 180 participants were randomly selected using convenience sampling. SEM-PLS, or structural equation model-partial least squares, was used to analyze the data. The findings of this study show no statistically significant correlation between organizational success and e-HRM. E-HRM and employee engagement, as well as employee engagement and organizational performance, have a strong beneficial association. The implications of the results highlight the need to establish good E-HRM practices to increase employee engagement and boost organizational performance.