The study aims to evaluate the Islamic banking prospects in Somalia and the role it can play in reviving the country’s ailing economy. Somalis were recruited through purposive sampling techniques. Structural equation modeling (SEM) has been adopted to analyze the data collected using SmartPLS. The findings have shown a negative and insignificant impact of absence of a functioning financial system, lack of funding and expertise, and effect of bank absence on businesses. Financial and political infrastructure significantly affected the Islamic banking system of Somalia. Law and order measures show a significant impact on suitability of banking and financial systems of Somalia. Sharia compliant finance shows a positive but insignificant impact on Islamic banking in Somalia. Standalone new banks in Somalia have fewer chances of success due to the absence of personnel with the necessary skills and qualifications.

Since late 2019 and throughout 2020, the global economy has been experiencing difficult times due to the outbreak of the lethal Coronavirus (COVID-19). This study looks at the financial impact of this epidemic on the global economy using Malaysian market index i.e. FTSE Bursa Malaysia KLCI before and during COVID-19. Measuring the financial impact of this epidemic on the Malaysia economy may help policy makers to develop measures to avert similar financial catastrophic impacts on the global economy. The study uses Sharpe optimal and naïve diversification model to solve a scenario that factors in the level of corporate social responsibility(CSR) exhibited before and during the epidemic to measure the financial impact on the stock portfolio. The results show that the emergence of COVID-19exacerbated the already weak Malaysian economy. Our findings may help the policy makers in Malaysia to develop and maintain techniques and policies that may mitigate the negative financial impact and handle similar epidemics in the future. Future studies could cover the financial impact of CSR using variable scoring and apply the portfolio model with practical and prevailing constraints.

The term “metaverse” refers to a virtual reality setting where users may engage in sustained and immersive interactions with other users and digital information. The metaverse offers new potential for entertainment, education, commerce, sociability, and creativity; therefore, it is anticipated to play a significant role in the future of the digital economy. However, the metaverse presents additional difficulties in resolving conflicts that can develop between its users, producers, and providers. Intellectual property rights, privacy, contract enforcement, fraud, harassment, and cybercrime are some of the concerns that may be raised in these conflicts. The existing legal system for settling these conflicts is disjointed and insufficient since it does not consider the metaverse’s unique qualities and complexity. This study investigates the present legal framework to provide fair and effective conflict resolution in the metaverse. It then establishes tenable fundamentals within the context of scientific and legal foundations. and propose a theoretical model named Hybrid Legal Framework for Metaverse Dispute Resolution (HLFMDR).

This study examined the perception of investors toward the factors of Artificial Intelligence (AI) that impact behavioral intention of AI adoption. TAM was used with the addition of three factors of subjective norms, trust in service and service provider, and training. Jordanian stock market investors with basic investment knowledge were selected as the study participants using a convenience sampling method. The analyzed data were obtained from 610 responses. Results demonstrated the significant impact of trust in service and subjective norms on AI. Attitudes, perceived ease of use and perceived usefulness showed statistical significance toward intention to use AI. Results also showed significant impact of training on perceived ease of use.

This study aims to investigate the impact of Business Intelligence Systems (BIS) on the quality of strategic decision-making in top-level management. The independent variables in this study are Data Quality, Data Visualization, and BI Management, while the dependent variable is the Quality of Strategic Decision-Making. Additionally, the study explores the moderator variable, BI Scope, to further understand the relationship between BIS and the quality of strategic decision-making. By providing valuable insights into the relationship between BIS and the quality of strategic decision-making, this study contributes to the existing body of knowledge on business intelligence and strategic decision-making. The findings show that BI Management, BI Scope, Data Quality, and Data Visualization have substantial and favorable correlations with the quality of strategic decision-making. Effective BI Management techniques contribute to higher decision-making quality, emphasizing the necessity of BI resource management. The study also underlines the importance of BI Scope as a moderator variable, demonstrating its impact on the connection between BI and quality of decision-making. In addition, the research shows that Data Quality and Data Visualization have a considerable influence on strategic decision-making quality. Using effective visualization tools and ensuring high-quality data improves the results of decision-making processes. The interaction impact between BI Scope and Data Quality, on the other hand, was determined to be non-significant.

With the hope of improving students' social interactions who have been diagnosed with Autism Spectrum Disorder (ASD) in Saudi Arabia, the current study set out to examine the effectiveness of peer-mediated therapies delivered through the ubiquitous social media platform Snapchat. Thirty kids with ASD and 15 generally developing peers participated in the study. Alterations in social communication difficulties and observable social interactions were evaluated before and after the intervention to determine its efficacy. The results of the research revealed significant improvements in both self-reported difficulties with social communication and the quality of participants' interpersonal relationships. These findings demonstrate the groundbreaking methodology's deep applicability and widespread cross-cultural significance. Individuals with ASD have shown to benefit greatly from individualized, technology-based therapy to improve their social interactions, as shown by this study.

Lesotho faces fiscal deficits due to high government expenditure as characterized by the huge wage bill regarded as one of the highest in the world (UNICEF, 2018). The aim of this research paper is to investigate the tax revenue impact as a share of Gross Domestic Product (GDP) on economic growth in Lesotho. This research is provoked by the theoretical postulations and empirical evidence that their relationship is positive up to a certain optimal level which is the point at which economic growth rate is maximized. The study used data from 1988 to 2017 to investigate the relationship between tax burden and economic growth in Lesotho by applying the log-linearized model of Scully’s tax optimization model transformed into an ARDL bounds testing framework. Granger causality test and error correction model was employed to investigate the long run relationship between tax burden and economic growth in Lesotho. The findings of study revealed a long-run relationship between economic growth rate and tax burden with a unidirectional causality running from economic growth to tax burden. Granger causality revealed no causal effect running from tax burden to economic growth in Lesotho despite expectation of significant causal effect following both theoretical and empirical literature from various studies. Error correction results further supported a co-integrating relationship running from economic growth to tax burden with 100 percent speed of adjustment in the short-run towards a long-run equilibrium level. An optimal tax burden could not be established as the variables of interest were negative and insignificant signifying the insignificance of tax policy in stimulating the economic performance.

The use of data mining to predict early employment readiness of students is gaining importance due to the expansion of data production in various industries. This study aims to address the employability issue in Middle Eastern nations by utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) data mining technology. The experimental investigation used data from tracer studies conducted by three Jordanian universities, consisting of 22 parameters. Results showed that despite achieving an accuracy of 94% for the graduate dataset, ANFIS exhibited high complexity due to the large number of attributes used. The study has implications for selecting relevant variables and investigating multiple aspects. Data mining has various applications, including classification, clustering, regression, association rule development, and outlier analysis. As data production continues to expand, this study provides insights into the potential use of ANFIS in predicting early employment readiness of students in Middle Eastern nations.

The present research examines the debated relationship between quality management and innovation using a multidimensional quality management perspective. The quality performance that is supposed to result from the adoption of quality management is investigated further as a possible mediator between quality management and innovation in the higher education sector. The data needed to test the hypotheses was gathered by sending a survey via the internet to the faculty members at universities in the United Arab Emirates. Applying the approach of structural equation modelling with partial least squares, the hypothesised associations between 175 respondents are evaluated. According to the findings, implementing rigorous quality management has a direct as well as indirect impact on innovation performance via its impact on quality performance. The impacts of soft quality management on hard quality management have indirect consequences on innovation performance. The association between rigorous quality management and innovation performance is moderated in part by quality performance. This study provides one of the initial studies to apply the multidimensional method of quality management in higher education and has the potential to assist directors in better comprehending the interdependencies between soft and hard quality practices.

This study has examined factors, such as technology and employee influence on artificial intelligence (AI) adoption of e-innovative projects in the United Arab Emirates. The present study revealed the success or failure of e-innovation adoption in the public sector of the UAE and hinted at potential e-innovative projects to consider essential factors before adopting it. The study's sample covered the government sector, and the data collection method was a survey questionnaire with a sample size of 1037 responses made up of government employees. This paper was mainly built upon the diffusion of innovation and technology acceptance theories. The analysis findings showed that technology (an external factor) significantly and positively contributed to adopting AI e-innovation technology. Further analysis revealed that employee (internal factor) proxies directly influenced the adoption of AI e-innovation technology. Overall, internal and external factors contributed to adopting e-innovation technology in the United Arab Emirates. For future directions, additional factors related to the market should be considered to explore their contribution.