Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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The Effect of Corporate Social Responsibility to Organizational Performance Among Selected Manufacturing Companies: Basis for Management Intervention Plan Proposal
No full textCurrently, corporate social responsibility (CSR) has been one of the crucial factors for manufacturing companies both in local and international settings. Despite CSR becoming more and more important, manufacturing companies have particular difficulties putting good CSR initiatives into action. The study determined the effect of corporate social responsibility to organizational performance among selected manufacturing companies as a basis for management intervention plan proposal. This hypothesis was rejected as corporate social responsibility implementation by the manufacturing company has significant effect to their current organization performance. The results showed the degree of organizational effectiveness of a corporation defines its performance in major part. Effective delivery plans depend on good lead time management since it helps one to track the delay between order placing and delivery execution. The study also revealed obvious differences between many degrees of CSR implementation, so underlining effective CSR projects. Top among the problems is including CSR into the main business plan and ensuring it aligns with overall corporate goals. Second and requiring clearly defined policies and supporting systems is managing complex criteria tied to social and environmental commitments. Third-ranked issue requiring accurate measurements and evaluation methods is knowing how CSR efforts effect company performance
Power, the Dark Triad, and the Organisational Tragedy of the Commons: Knowledge Retention as an Instrument of Domination
No full textThis article discusses the exercise of power within organisational settings, with emphasis on dysfunctional behaviours associated with the Dark Triad — Machiavellianism, narcissism, and subclinical psychopathy — and on the deliberate retention of knowledge as a strategy of symbolic domination. It is based on the understanding that pursuing power is a legitimate human inclination, but one that, in competitive organisational contexts, can take on manipulative and harmful forms. The study draws on political philosophy and organisational psychology contributions to analyse practices such as the selective favouring of allies, the manipulation of success trajectories, and the restriction of access to knowledge as a means of self-preservation and status maintenance. It also considers the influence of Brazilian cultural elements — marked by centralised leadership styles and low levels of cooperation — which contribute to the reproduction of permissive environments that enable opportunistic profiles to thrive. The study further highlights a gap in the main strands of management theory, which, by prioritising material incentives and formal structures, tend to overlook the effects of symbolic power relations and interpersonal manipulation on organisational performance. It is argued that the absence of precise institutional mechanisms to address such behaviours reflects a lack of theoretical recognition of the problem. The article concludes that understanding knowledge retention as an instrument of domination requires a broader perspective on power dynamics and the incorporation of interdisciplinary approaches that combine ethics, organisational culture, and individual behaviour. Further empirical investigation is recommended, particularly with support from organisational psychology
Deep Learning for Financial Markets: A Case-Based Analysis of BRICS Nations in the Era of Intelligent Forecasting
No full textIn this paper, we develop a method based on a deep learning method in financial market prediction, which includes BRICS economies as the test cases. Financial markets are rife with volatility that is affected by a "bed of complexity," coddled by local and distal factors. To leverage these vast datasets both deep learning models such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks as well as hybrid architectures are used in this study. The paper evaluates the predictive accuracy of the models, and by so doing, identifies their strengths in predicting temporal dependencies and intricate market patterns. In particular, deep learning techniques are applied to case studies of individual countries in the BRICS to highlight the application of deep learning to disparate country specific problems, such as liquidity crises and market shocks. These findings show that classical statistical methods are outperformed by deep learning systems in a precise and reliable financial forecasting. This research highlights the ability of AI driven systems to change financial decision making processes, improving investor confidence and improving economic stability in BRICS nations. This study also readers the value of deep learning in financial market analysis, especially in economies in the developing countries. Application of techniques and architectures e.g. Convolutional Neural Networks (CNNs) that excel at identifying spatial patterns, and Long Short-Term Memory (LSTM) networks renowned for their prowess on sequential and time series data, for real world market prediction are explained. In addition, the research discusses hybrid architectures which extend knowledge, fusing strengths of both architectures to improve prediction accuracy and how deep learning develops to solve particular financial challenges. Through reading these notes readers get exposed to data preprocessing techniques such as normalization and feature selection which are important for boosting deep learning performance. The paper also includes an introduction to the evaluation of models using MSE and R-squared values for validating them in terms of reliable outputs. This research combines deep learning theory and practical case study to offer a useful educational resource for students, researchers, and practitioners who want to apply AI in financial forecasting in complex and dynamic global markets
Comparative Analysis of Currency Exchange and Stock Markets in BRICS Using Machine Learning to Forecast Optimal Trends for Data-Driven Decision Making
No full textThe BRICS nations’ economies show that the countries are global financial powerhouses whose currency exchange rates and stock markets have influence globally. In this paper, the analysis of the forecast trends in both Currency Exchange and Stock Markets using a dual layered machine learning approach exposing models such as Long Short Term Memory (LSTM), Random Forest, Gradient Boosting and Support vector machines (SVM) is conducted. Their performance is tested twice, first on currency exchange and then on stock market data, to compare them on the basis of predictive power to deliver actionable insights. Each model is applied to currency and stock market data, separately, as the study mainly uses extensive historical datasets from BRICS economies. Benchmarking is done using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-squared values. For currency exchange, LSTM turned out to be the most effective model as it can handle a sequence of time series data. The best performance for stock market forecasting was achieved by Gradient Boosting, which is adept at finding complex nonlinear relationships. Random Forest proved to be consistent across both Datasets but SVM was found to be challenged on Scalability and Data Complexity, with relatively lower accuracy. The research goes on to repeat the comparative analysis for each of the different models, to illustrate the subtle differences between machine learning techniques in their capacity to effectively process financial datasets of all varieties. Predictive accuracy and reliability is further enhanced to reconcile conflicting trends between currency and stock markets by creating an ensemble model of all algorithms. These findings provide a robust framework for informed decision making for stakeholders to identify the more stable and hence more profitable market in the BRICS context. The results of this study add to the expansion of application of machine learning to global finance by demonstrating how tailored algorithms can offer significant economic planning and investment strategy plans
The Effects of Unemployment on Economic Growth in Saudia Arabia in the period 1995-2023
No full textThis study examined the relationship between unemployment and economic growth in Saudia Arabia for the period 1995 to 2023. The study utilized co-integration and error correction model approach. Although the unit root tests showed that the variables were integrated of different orders, the Johansen co-integration result showed that the variables were co-integrated. This study has revealed that unemployment, growth in government expenditure and gross fixed capital formation, population growth and education among others are significant explanatory variables of economic growth in Saudia Arabia under the period of study. Also, the result of the Error Correction Model analysis (ECM) shows that the unemployment has a negative and insignificant impact on economic growth over the period under study. Suggesting that higher unemployment leads to decreased GDP growth, indicating that unemployment rate increases economic growth becomes decline. The results of the study, show that, there is no causality relationship between unemployment and economic growth over the period under study. In addition, the results of causality test show that there is evidence of unidirectional causality running from growth government expenditure, the gross fixed capital formation, education (literacy rate) and population growth to economic growth (GDP) at different confidence and level of significance in Saudi Arabia. Moreover, this study present evidence that, bidirectional causation between unemployment and growth of population was found in Saudia Arabia. To increase economic growth, Saudi Arabian government should identify measures to reduce the unemployment rate and improve country\u27s economic growth. For example, improving the quality of education, skills training, and implementing employment policies. The contribution of the study is the confirmation of the existence of the correlation of unemployment with the mentioned development indicators, and the validity of Okun\u27s Law also hold on Sudia Arabian economy
CEO Tenure, Narcissism, and Greed: Do They Influence Corporate Tax Avoidance?
No full textThis study aims to analyze the influence of the existence of independent commissioners, tenure CEOs, narcissism CEOs, and greedy CEOs on tax avoidance in manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2019–2023 period. Tax avoidance is a company\u27s strategy to reduce the tax burden legally, but it often causes controversy related to business ethics. This study uses secondary data from the company\u27s annual report which is analyzed by multiple regression method. The results show that CEO narcissism and greedy CEOs have a significant positive influence on tax avoidance, indicating that the higher the level of narcissism and greed of CEOs, the greater the tendency of companies to avoid taxes. Furthermore, the existence of independent commissioners and the CEO\u27s tenure have a significant positive effect on tax avoidance. This finding has implications for regulators and stakeholders to strengthen the supervisory mechanism over the CEO\u27s strategic decisions in corporate tax management. In addition, this study emphasizes the importance of the role of independent commissioners in maintaining corporate tax transparency and accountability
Predictive Insights: Using Macro and Micro Models for Wage Growth Forecast in Malaysia
No full textThe Malaysian government has implemented the Progressive Wage Policy (PWP) to accelerate wage growth and address the low contribution of employee compensation (CE) to Gross Domestic Product The objective, as outlined in the Twelfth Malaysian Plan (RMKe-12), is to achieve a median wage of RM2,700 per month by 2025 and attain an annual productivity growth rate of 3.7% from 2021 to 2025[22]. In line with this policy, Social Security Organization (PERKESO), an organization under the Ministry of Human Resource, has taken proactive measures to analyze and model wage growth forecasting for the upcoming years. This paper aims to develop a forecasting model by examining the relationship between wages and various macroeconomic and microeconomic variables, including the unemployment rate. The methodology employs both Phillips Curve and Artificial Intelligence Model to predict wage increments, covering the period from 2016 to 2023. The approach ensures the development of a robust model supported by big data. This study establishes a predictive relationship within a stylistic framework of wage bargaining, indirectly fostering dynamic ecosystems between the prevailing economic conditions and employers\u27 market trends in the Macro Model. The model considers the institutional structure of the current economic condition and employers\u27 market trends, incorporating factors based on economic indicators and contributions. Additionally, a Machine Learning Gradient Boosting Regressor Model is utilized to predict the output from micro models. This enhances the overall reliability of the model. Significantly, the methodological innovation revolves around the integration of Macro and Micro Models, utilizing detailed data from job placements and monthly contributions spanning from 2020 to 2023 for the wage forecast framework. This distinct approach facilitates forecast development through model averaging techniques customized to maximize the accuracy of wage increase and estimated salary predictions
The Role of Machine Learning in Forecasting U.S. GDP Growth after the COVID-19 Pandemic
No full textThe COVID-19 pandemic resulted in one of the most recent economic shocks, impacting global trade, financial markets, and consumer behavior. In the US, GDP suffered a historic downturn in 2020, followed by an unbalanced recovery. Precise GDP growth forecasting has become increasingly essential for policymakers, businesses, and investors making decisions in the post-pandemic economy. Classic models, including Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR), and Dynamic Stochastic General Equilibrium (DSGE), have been popularly employed for GDP forecasting. Machine learning (ML) provides a dominant alternative, with the potential to handle enormous amounts of real-time data, sense non-linear patterns, and handle economic shocks more effectively than traditional approaches. This paper delves into the potential of ML in GDP forecasting, touching on some key techniques, including neural networks, ensemble learning, and deep learning. This paper assessed the accuracy of two machine learning models, Random Forest (RF) and Long Short-Term Memory (LSTM), in forecasting U.S. GDP growth during the post-COVID-19 pandemic. Although ML-based forecasting holds prominent advantages, challenges, including data quality, explainability, and ethical issues, must be resolved for increased usage in economic decision-making
Securing Retrieval-Augmented Generation Pipelines: A Comprehensive Framework
No full textRetrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of Large Language Models (LLMs) by enabling them to access and incorporate external knowledge sources, thereby improving response accuracy and relevance. However, the security of RAG pipelines remains a paramount concern as these systems become integral to various critical applications. This paper introduces a comprehensive framework designed to secure RAG pipelines through the integration of advanced encryption techniques, zero-trust architecture, and structured guardrails. The framework employs symmetric and asymmetric encryption to protect data at rest and in transit, ensuring confidentiality and integrity throughout the data lifecycle. Adopting zero-trust principles, the framework mandates continuous verification of all entities within the data flow, effectively mitigating unauthorized access and lateral movement risks. Additionally, the implementation of guardrails, such as immutable system prompts and salted sequence tagging, fortifies the system against prompt injection and other malicious attacks. A detailed lifecycle security continuum is presented, illustrating the application of these security measures from data ingestion to decommissioning. Case studies across healthcare, finance, retail, and education sectors demonstrate the framework’s effectiveness in maintaining high performance and scalability without compromising security. This work provides a foundational model for future research and practical implementation, emphasizing the necessity of robust security protocols in the deployment of RAG-based applications
Design And Implementation of a Smart Wireless Parking System
No full textNumerous people choose private vehicles over public transportation due to the fast growth of metropolitan populations and hectic work schedules. Finding parking spaces, particularly for cars, is a common challenge that sometime results traffic congestions too. Drivers frequently don\u27t have the time to look for parking spots because of their busy schedules. A digital system that can automatically identify and give signals of available open parking spaces is therefore becoming more and more in demand. Such a method would save a great deal of time as the world is growing towards digitalisation, which is important in the fast-paced generation of today. In order to show available parking spaces in a certain region, this paper explains how data is gathered from several neighbouring parking lots and transmitted to a central unit. The nRF24L01 transceiver module provides full-duplex RF communication that is both economical and effective. These modules are reasonably priced and easy to control. The goal of this paper is to clearly indicate the exact location of available parking spots within a parking area. A list of empty slots will be displayed at the entrance. The system comprises a control unit, IR sensors, an LCD screen, antennas, and more. Implementing this system for car parking will significantly reduce the need for manpower