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This study examines the impact of Big Data technologies on operational efficiency in DP World’s supply chain and logistics operations. Employing a mixed-methods approach, we surveyed 405 stakeholders and performed 20 semi-structured interviews to investigate the incorporation of advanced analytics, machine learning, and real-time processing capabilities. The study employs Technology-Organization-Environment (TOE) and economic frameworks to examine the technological, organisational, environmental, and economic determinants affecting Big Data adoption. Results indicate substantial positive effects on the reduction of operational complexity, enhancement of customer responsiveness, improvement of supplier connections, and strengthening of technical partnerships. PLS-SEM analysis utilising ADANCO software validated all hypotheses, demonstrating robust connections between Big Data deployment and supply chain efficiency measures. Essential findings indicate that governmental assistance, organisational culture, and information technology infrastructure are crucial success determinants. The report offers practical advice for supply chain managers and policymakers, emphasising Big Data's revolutionary capacity for competitive advantage in global logistics operations.

This research investigates the contribution of women-owned businesses in driving economic development within the UAE, focusing on the primary elements influencing their success. A purposive sampling method was used to gather input from the Emirati businesswomen through a custom-designed questionnaire. Data were collected via various methods, including telephone interviews, email correspondence, and on-site visits where applicable. For analysis, the study employed partial least squares regression and structural equation modeling to assess both the measurement and structural frameworks. The evaluation centered on four key dimensions: individual attributes, organizational internal and external elements, support mechanisms, and the impact of national cultural norms. According to the results, business outcomes are positively influenced by individual traits, cultural context, external conditions, and support networks, whereas internal factors appear to lack significant influence. Notably, while the prevailing national culture enhances the overall performance of women-led ventures, it appears to impair the function of support systems. The research sheds light on the essential enablers and cultural limitations shaping the success of female entrepreneurs in the UAE.

Gauteng, South Africa's preeminent economic hub, confronts the imperative of achieving sustainable infrastructure development amidst rapid urbanization. The strategic integration of smart initiatives is crucial for fostering economic growth, environmental resilience, and societal well-being. This paper presents a critical review of Gauteng's infrastructure sustainability efforts through smart initiatives, employing a conceptual model that delineates Infrastructure Sustainability Development (ISD) as the dependent variable. Four independent variables—Smart Energy Systems, Smart Transportation, Smart Water Management, and Smart Urban Planning—are examined, each further defined by five sub-variables to ensure a comprehensive evaluation of their impact. This analysis evaluates the efficacy of provincial smart infrastructure initiatives, including the flagship Lanseria Smart City project, in promoting sustainability and aligning with the United Nations Sustainable Development Goals (SDGs). The study draws upon relevant theoretical frameworks, including Sustainable Development Theory, Systems Theory, Innovation Diffusion Theory, and the Smart Cities Framework, to provide a robust analytical foundation. The findings highlight achievements, challenges, and prospects, culminating in policy implications and recommendations for enhancing sustainable infrastructure development in Gauteng.

The overriding purpose of the study was to ascertain through empirical research whether Porter’s diamond firm strategy, structure and rivalry construct influence the key automotive industry of Pakistan. The Researchers applied positivism research philosophy to help generalize the research study outcomes. Descriptive analytics was deployed to help determine the significance, direction and magnitude of the study. Primary data was obtained from senior managers working in the industry by applying a structured questionnaire for subsequent analysis. While soliciting data, an 85.6% response rate was documented. Statistical techniques were applied which included parametric and nonparametric to arrive at objectively based results to draw safe conclusions.  The research findings have created significant value for practitioners in the auto industry as well as the decision-makers at the helm of policy formulation. Pragmatic inferences to be drawn from the research findings include improving the performance of the automotive sector by internalizing Porter’s diamond construct firm strategy, structure, and rivalry. Results in other industries may be tested with future research to help establish generalizability in the area.

The prime purpose of the research was to examine, through empirical study, and help determine the government’s role as moderator in the relationship between Porter’s diamond firm strategy, structure, and rivalry construct and the firm’s performance in Pakistan’s automotive sector. In the study, the research philosophy was positivism, and a deductive approach was applied as the main reason to establish generalizations in the results. With a view to determining the magnitude, direction, and significance, researchers emphasized descriptive and inferential statistical techniques. Data was solicited from 166 sample respondents through a structured questionnaire. To establish the reliability perspective and validity context of the questionnaire constructs, a pilot study was part of the research study. Parametric and nonparametric statistical techniques also became an integral part of analysis for drawing objectively based conclusions. For moderation purposes, the tool was macro, Process v3.0 (model 1). The study findings aptly indicate that in a theoretical context, the relationship is not significant, whereas the practical perspective reflects that government policy does influence industry firms’ performance; however, the mediating role of government between Porter’s constructs, viz., firm strategy, structure, and rivalry, and industry firms’ performance documented no impact. In the constituencies of academia, automotive sector players, and policymakers, the moderation results shed light on theoretical and practical contextualization.

This study aims to enhance credit risk identification, improve loan borrower review efficiency, and increase default prediction accuracy rate using data mining and machine learning techniques. The study also employs deep learning to develop a consumer loan default prediction model that minimizes credit risks and ensures consistent development. The researchers collected data from a survey of 1000 participants, stratified into local and foreign banks, and selected the top 11 banks based on turnover and customer volume. To construct the machine learning model, Keras, a neural network library that runs on TensorFlow, was utilized. The model predicts loan applicant default likelihood. The study's practical implications demonstrate a noteworthy success rate of customer default prediction, which can significantly benefit banks. The model was evaluated on a test set of 250 records and achieved a test set accuracy of 95.2%, correctly predicting the default state of 238 out of 250 respondents.

The paper examines four main sources of financial risk exposure for four banks in the United Arab Emirates during the years 2003 to 2009. It begins by measuring stability levels for different types of financial risks using the coefficient of variation measure. The research then proceeds to investigate the nature of the relationship between financial risk and the actual reported financial performance of each company, namely the return on equity and return on assets. The findings show that all four sources of financial risk showed significant stability while both profitability indicators exhibited significant instability over the same period. Moreover, performance on the return on assets indicator was strongly and positively tied to a company’s return on equity indicator across the four banks but exhibited little or no interconnection with the four types of risks that banks are commonly exposed.

The purpose of this study was to analyze the dividend performance of eight UAE based banks between the years 2001 and 2005. The analysis was undertaken by examining three sets of financial ratios that are routinely used to measure bank dividend performance. The main ratios that were employed put a particular focus on the banks’ ability to pay dividends in relation to market value of its share, earnings and ability to cover the payments. Descriptive statistical analysis was used to rank the performance, measuring the dispersion and the stability-variability of the indicators. Conclusions were then drawn from the computation of the descriptive analysis of ratios that allowed the author to make an effective comparison of said banks. This type of analysis was used to summarize the performance of each bank based on three criteria, mean, standard deviation and coefficient of variation of each banks performance. The findings showed that eight banks performed reasonably well during the period studied, but have different strategies in paying their dividends across the years under study. The analysis reveals that these banks have been affected by both internal and external factors.