Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Leadership Styles and Employee Motivation: A Quantitative Study in a Chinese Call Center
No full textThis study examines the influence of different leadership styles—transformational, transactional, and laissez-faire—on employee motivation in a Chinese call center environment. The research aims to identify the dominant leadership style, assess employee motivation levels, and determine the relationship between leadership approaches and motivation. A quantitative method was employed using a Multi-Factor Leadership Questionnaire (MLQ) and the Workplace Extrinsic and Intrinsic Motivation Scale (WEIMS). The findings indicate that transactional leadership is the most prevalent style, but transformational leadership has the strongest positive correlation with employee motivation. The study suggests that a combination of transformational and laissez-faire leadership styles could enhance employee motivation and job satisfaction
Factors Influencing Filipino Online Shoppers’ Livestream Purchases
No full textIn today’s constantly evolving world of digital retailing, another marketing trend has emerged popularly known as livestream commerce which is making waves globally by reshaping shopping dynamics. This interactive channel combines the convenience of the conventional virtual shopping and the real-time engagement of live video which retailers may consider in reaching their target markets. Grounded on the Technology Acceptance Model, this study intends to address the existing research gap in exploring the variables that affect online shoppers’ livestream purchase behavior in the Philippines. Specifically, it is focused on examining the influence of perceived usefulness and perceived ease of use on livestream purchase intention. A purposive sampling was employed with 410 Filipino live shoppers as respondents through a validated online self-administered questionnaire. The hypotheses formulated were tested by applying inferential statistics. The empirical findings reveal that perceived usefulness and perceived ease of use have significant influence on livestream purchase intent which in turn, impacts actual livestream purchase behavior. The outcomes of this investigation offer meaningful insights to retailers and livestream application developers in devising strategies that will deliver a more immersive, engaging, hassle-free shopping journey. Likewise, this inquiry opens opportunities for future research on livestream commerce that can help strengthen its development in the country as well
Critical Assessment of Training and Development Practices in China\u27s Pharmaceutical Fund and Supply Agency: Challenges and Strategic Recommendations
No full textThe primary objective of this paper was to evaluate the training and development practices at a pharmaceutical fund and supply agency. To achieve this, a sample of 208 non-managerial staff members and 10 managerial staff members was selected using a simple random sampling technique. Data collection methods included self-administered questionnaires, interviews, and data analysis. The questionnaire data was analyzed using descriptive statistics, specifically frequency and percentage. Additionally, the interview and document review data were analyzed to identify patterns and themes from the participants\u27 responses. The findings of the study revealed several weaknesses in the agency\u27s training and development practices, including inadequate selection criteria, ineffective training methods, insufficient training duration, insufficient training content, and a lack of a comprehensive training policy. Notably, the agency did not prioritize pre-training evaluations, which could have helped assess the cost-benefit of the human resource training and development program. On the positive side, the agency\u27s management development programs have shown significant relevance in enhancing the current job performance of its staff. However, the document review revealed the \u27absence of a training development section or unit with qualified staff and adequate financial resources to facilitate training and development functions. To address these weaknesses, the agency should leverage its strengths and implement clear and scientific principles for human resource training and development. Frequency distribution was used to present the individual results of the study. Relevant literature was also reviewed to support the findings
Intellectual Capital, Islamic Work Ethics, and Organizational Performance of Baitul Maal Wat-Tamwil in Indonesia
No full textThe purpose of this study is to analyze the influence of intellectual sub-componenet and Islamic Work Ethics on the organizational performance of BMT (Baitul Maal Wat Tamwil) in Indonesia. Furthermor design of methodology for the research from Data was collected through a structured questionnaire distributed to a research population consisting of directors and staff employees representing Baitul Maal Wat-Tamwil institutions across Indonesia, with a sample size of 200 respondent. The sampling technique employed was purposive sampling, with specific criteria. The Baitul Maal Wat-Tamwil must have official legal status, possess a financial statement, and have been established for at least two years. The questionaires were distributed both directly to the Baitul Maal Wat Tamwil offices and via Google Forms to gather data for analysis. Partial Least Squares Structural Equation Modeling (PLS-SEM) software to evaluate hypotheses analyzed using. The Findings section result of this study indicates that Relational Capital, Structural Capital and Islamic Work Ethics have a significant positive impact on organizational performance, while Human Capital (HC), Social Capital (SC), and Spiritual Capital (SPC) do not have to affect the organizational performance of BMT in Indonesia. The last practical implications of the findings and results of this study hold significant relevance and importance for BMT in Indonesia because it provides comprehensive directions related to supporting factors of organizational performance through the lens of the concept of intellectual capital component theory and Islamic Work Ethics. This finding helps the BMT institution and the regulations that oversee it, namely the Ministry of Cooperatives and Small and Medium Enterprises and the Financial Services Authority as a direction for effective solutions to find out the challenges and weaknesses that hinder its performance, with the hope of taking a large role in serving the needs of small and medium communities, especially in Indonesia, so that consumers can also feel the contribution from the results of this finding
The Next-Gen Finance Business Partner: Thriving in the Age of AI and Business Intelligence
No full textThe responsibilities of Finance Business Partners (FBPs) are shifting as a result of the revolution that has been brought by the implementation of Artificial Intelligence (AI) and Business Intelligence (BI) systems in the past few years. The role of the FBPs has been transformed in the process of moving from conventional qualitative analysis to a more strategic role that can facilitate the automation of many tasks, enhance the forecasting functions and offer real-time decision-making, allowing the FBPs to focus on value added work such as strategy, implementation and management, and integration with other areas of the organization. However, the integration of AI and BI is not without some challenges, which include resistance to change, data security risks, and a skills shortage. The importance of increasing the technical skills of the FBPs, the need for a strong partnership between the finance and operational teams and the need for strong ethical governance frameworks to guide the use of AI are also discussed. This paper also includes real world examples of how organizations are employing AI and BI to enhance their forecasting, improve the effectiveness of their financial processes and, most importantly, achieve their strategic objectives. Therefore, the results of this research support the concept that FBPs can be useful peers in relation to AI and BI if they adopt the technological tools and overcome the barriers to their usage. As a result of the findings, several practical recommendations are provided for FBPs to succeed in this evolving environment
Data-Driven insights on the relationship between BRICS financial policies and global investment trends
No full textThis study investigates the dynamic relationship between the financial policies of BRICS nations—Brazil, Russia, India, China, and South Africa—and global investment trends. As emerging markets like the BRICS play a crucial role in the global economic growth, it is critical to understand how changing in the financial policies in these markets interact with international investment flows for both investors and policymakers. The study leverages data of economic indicators, policy measures, and global investment patterns by building regression, decision trees and deep learning models based on advanced machine learning techniques, including regression models, decision trees, deep learning methods such as Long Short-Term Memory networks and Transformers. According to the findings, there are strong correlations between fiscal, monetary and trade policies in the BRICS economies and agent behavior in the global capital market. Uncovering these patterns therefore provides actionable insights for investors to navigate the changing finance terrain of those countries better and advice for policymakers on the way to fashion policies that would attract investment. This research supports the use of data driven technique to capture the intricate economic relationship and investment prediction outcomes in the case of BRICS financial systems
Optimizing E-Commerce Platforms with AI-Enabled Visual Search: Assessing User Behavior, Interaction Metrics, and System Accuracy
No full textThe integration of artificial intelligence (AI) into e-commerce platforms has revolutionized user interaction, with AI-enabled visual search emerging as a transformative tool for enhancing product discovery and customer engagement. This study explores the impact of AI-driven visual search systems on user behavior, interaction metrics, and system performance in digital commerce. Utilizing a mixed-methods approach, the research evaluates system architecture, user satisfaction, accuracy metrics, and ethical considerations through comparative analysis of keyword-based versus image-based search models. Results indicate that visual search significantly improves user satisfaction (by 85%), reduces task completion time (by 38%), and enhances precision and recall metrics across all evaluation parameters. The study also highlights the importance of explainable AI (XAI), multimodal interaction analysis, and cybersecurity frameworks to ensure fairness, transparency, and secure data handling. Strategic recommendations emphasize the adoption of multimodal interfaces, adaptive learning, and ethical AI governance. The findings underscore the pivotal role of visual search in optimizing e-commerce performance and user-centric digital experiences
Predictive Analytics for Telecom Customer Churn: Enhancing Retention Strategies in the US Market
No full textThe telecommunications industry in America has been characterized by exponential technological advancements and escalated competition, leading to heightened client expectations. Consequently, client retention has emerged as a crucial metric for telecom companies, directly influencing profitability and market share. The chief objective goal of this study was to build strong predictive models that could correctly identify at-risk customers in the US telecom market. This research paper aimed to use machine learning algorithms and advanced data analytics to uncover patterns and trends in customer dissatisfaction or intent to churn. This study centered particularly on the American telecom market, examining relevant client data drawn from various sources, entailing billing records, client service interactions, and usage patterns. The dataset for the current study was retrieved from proven and verified sources. This dataset provided intensive insight into customer behavior in terms of churning in the telecom industry. It contained highly elaborate information on customer demographics, service usage, and several indicators that are substantial for the analysis of customer retention and churn. The dataset was designed for the exploration of factors that influence customer churn and retention. The given dataset provided a very good basis for building predictive models aimed at finding customers who are at risk and understanding the dynamics of customer turnover. Among the different models that can be used are Logistic Regression, Support Vector Machines, and Random Forests, among others, each with its advantages and disadvantages. The Random Forest algorithm attained the highest accuracy, indicating exceptional performance in effectively identifying both churn and non-churn instances
EEG Functional Connectivity and Deep Learning for Automated Diagnosis of Alzheimer\u27s disease and Schizophrenia
No full textElectroencephalogram (EEG) functional connectivity analysis provides important clues about brain network abnormalities, an important approach to diagnose complex neurological diseases such as Alzheimer’s disease and schizophrenia. Advanced computational analysis can effectively analyze disorders with unique disruptions in neural connectivity. Deep learning (DL) is one of these, and has emerged as a powerful tool to facilitate automation in diagnostic processes and accurate classification by the use of DL models. The application of DL techniques and EEG functional connectivity metrics for the automated diagnosis of Alzheimer’s disease and schizophrenia is investigated in this study. For analysis, EEG data from patients with these disorders were used. To quantify the interregional synchronization of neural activity, functional connectivity metrics, such as coherence and phase locking value were extracted. Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks based multi class classification framework was designed to detect patterns related with the disorders. Results demonstrated DL framework performance at 94% for Alzheimer’s disease and 91% for schizophrenia. The DL models were then found to robustly replicate such inter-regional disruptions, with connectivity patterns analyzed via connectivity maps, revealing distinct inter-regional patterns in both conditions. This has also been demonstrated by the superior performance of DL methods in processing EEG data with complex and high dimensionality, and in extracting informative features for diagnosis. Finally, EEG functional connectivity metrics and DL methods greatly increase diagnostic accuracy for Alzheimer’s disease and schizophrenia. These findings point towards the transformative power of AI driven solutions in clinical diagnostics to achieve scalability and efficiency in neurological disorder diagnosis. Future research should be directed towards gap expanding application level of these models to other neurological conditions, and refinement of frameworks that can be implemented in a clinical setting
Artificial Intelligence in Multi-Disease Medical Diagnostics: An Integrative Approach
No full textWith advanced algorithms, artificial intelligence (AI) has revolutionized the medical diagnostic field where diseases can be predicted simultaneously. The integrative nature of this approach is novel because it can better encompass the complexity of comorbid conditions that are so common in patients; thus, addressing them in a more holistic diagnostic tone that is lacking in previous works. In this study, the investigation of the usage of AI models for simultaneously diagnosing diseases like diabetes, cardiovascular conditions, and neurological disorders is done. Therefore, based on AI techniques i.e. artificial neural networks (ANNs) and ensemble learning methods, a multi-disease diagnostic framework was developed to achieve this. A variety of features, related to each condition, were captured from multi-modal datasets including imaging, laboratory test results, and patient histories. The system was developed to manage the big flow of aggregated data and offer detailed diagnostic views of many diseases. Sensitivity, specificity, and overall diagnostic accuracy were used to evaluate the framework\u27s performance. The results showed that the AI framework has high diagnostic accuracy for all targeted conditions an overall sensitivity of 93% and a specificity of 91%. Importantly, the combination of multi-modal data proved to substantially improve the system’s ability to identify and distinguish comorbid conditions. It makes the importance of using various data sources to benefit from the reliability and comprehensiveness of AI diagnostics obvious. Overall, AI-driven multi-disease diagnostic systems provide great promise for the role of delivering potentially transformative clinical healthcare workflow improvements, reducing errors, and improving patient outcomes. These frameworks will need to be scaled and tested in various healthcare settings and also across more varied diseases to help make medical diagnosis more available and effective