Statistical performance of agglomerative hierarchical clustering technique via pairing of correlation-based distances and linkage methods

Agglomerative hierarchical clustering technique assists to group unknown objects into several clusters. The quality of clustering can be achieved when the clusters are internally homogeneous and externally heterogenous. However, the technique is sensitive to the choice of the distance measure and linkage method, particularly to the correlation-based distance because it is influenced by outliers. Choosing the wrong pairing may lead to the poor cluster formation and low clustering performance. Therefore, the main objective was to measure the statistical performance of agglomerative hierarchical clustering using four correlation-based distances and four linkage methods, tested through simulation study and real data application. The four correlation-based distances; Pearson, Spearman, Kendall’s Tau and Winsorized while the four linkage methods; Average, Ward’s, Complete and Single. The simulation study was conducted under various data conditions; number of variables, sample sizes, percentages of outliers, and data distribution to measure the performance using Cophenetic Correlation Coefficient (CCC). To validate the clustering model on real data, the Spearman-average algorithm was applied to cluster Juru river basin data based on five water quality parameters. The simulation study showed that the Spearman-average performed well under normal distribution and contaminated data, while the Winsorized-average performed well under Gamma distribution. Five tables of summary for choosing appropriate clustering algorithms according to data distribution were produced. The real data validation produced five clusters, with the CCC of 0.76 and the Calinski and Harabasz index of 26.879. In addition, the clustering model was also able to identify the time when the highest and lowest level of river contamination occurred. As a conclusion, the performance of clustering depends on the conditions of the data. Agglomerative hierarchical clustering is suitable to be performed when the number of variables is not more than 20 and the sample size smaller than 500

Determinants of non-performing financing in local and foreign islamic banks in Malaysia: the moderating role of staff efficiency

Non-Performing Financing (NPF) levels recorded by 11 local and 5 foreign Islamic banks in Malaysia from 2017 to 2023 indicate fluctuations throughout that timeframe. High NPF may impact the financial performance of Islamic banks in Malaysia and effect the general financial stability of the country. This study examines the determinants that may influence the NPF levels of both local and foreign Islamic banks in Malaysia. It includes internal factors such as Return on Assets (ROA), Financing to Deposit Ratio (FDR), Bank Size, and Financing Loss Provision (FLP), and external elements like Gross Domestic Product (GDP), Unemployment Rate (UR), Inflation Rate (IR), and Control of Corruption (COC). This research also includes Staff Efficiency (STAFFX) as a moderating factor that affects the link between the independent and dependent variables. Staff Efficiency is important because if personnel do not work effectively (due to insufficient expertise), the performance of the bank is likely to be significantly impacted. The data for this study were sourced from the Fitch Connect Database, the Department of Statistics Malaysia, and the Worldwide Governance Indicator. This research employs Panel Data Regression Analysis with the Generalized Least Squares Method and a hierarchical regression model. The findings indicate that ROA, FDR, Bank Size, FLP, and UR significantly influence the NPF levels of local and foreign Islamic banks in Malaysia. Other predictors, namely GDP, IR, and COC have insignificant impact on NPF. This research also shows that Staff Efficiency can moderate and enhance the correlation between FDR and NPF in local Islamic banks, along with the links between Inflation Rate and NPF as well as COC and NPF in foreign Islamic banks. This study provides new interesting findings regarding the beneficial effect of Staff Efficiency in minimizing NPF during the unstable economic conditions

The optimization of ruminant feed with local ingredients using linear programming

The high cost of feed is a major factor that reduces profit margins in ruminant farming, primarily due to the reliance on imported feed pellets. However, various locally available ingredients present cost–effective alternatives that can still meet the nutritional requirements of ruminants. This study aims to create a cost–effective goat feed using local ingredients through Linear Programming (LP). The objective is to find the cheapest feed mix that still meets all the nutritional requirements. Eleven local ingredients from Kedah and Perlis were chosen for their availability, cost–effective, and nutritional value. Using Excel Solver, a LP model was formulated and solved to meet key nutrient needs for ruminants, specifically dry matter, crude protein, calcium, and phosphorus. Nutrient composition data for each ingredient were collected from The Malaysian Agricultural Research and Development Institute (MARDI). The results show that a balanced feed can be formulated using local ingredients, reducing reliance on imported feed and supporting sustainable farming in Kedah and Perlis. The LP model found an optimal solution using just two local ingredients, petai belalang and napier grass, that fully meet all nutritional needs. This approach resulted in a 72% reduction in feed costs compared to current commercial options. This research offers a practical solution for local farmers, enabling the production of ruminant feed at a lower cost and thereby enhancing profit margins. As a result, this contributes to improved food security, economic resilience in rural communities, and a more sustainable livestock industry

i-Synergy: An integrated predictive model of time pressure, personality types, gender, knowledge and task complexity to determine software developer’s performance

Human factors play a crucial role in software engineering (SE) as software is developed and utilized by people. One of the key reasons for software project failure is not assigning the right people to the right tasks during project planning. This issue becomes critical when developers work under time pressure (TP), often resulting in poor performance and delays. Each personality type approaches TP differently, and gender-based personality differences may further influence how developers handle TP, leading to varied outcomes. In addition, task complexity and developers’ knowledge interrelate with personality types and gender, potentially affecting project performance under TP. The main aim of this study is to propose the i-SYNERGY model by investigating the relationship between TP, personality types, gender, knowledge, and task complexity. To develop this model, empirical evidence was gathered from controlled experiments conducted with SE students, and generalised from industrial data through two case studies. The Myers-Briggs Type Indicator (MBTI) and NASA task load index (TLX) were used to measure personality types and TP, respectively. The data analysis was divided into two stages. The first stage involved examining factual figures of data to develop the model, while the second stage involved predictive experiments for developing the model under the knowledge discovery in databases (KDD) process. Five data mining techniques—artificial neural network (ANN), support vector machine (SVM), decision tree, K-nearest neighbor (KNN) and logistic regression were employed to identify the most suitable technique for model development. Logistic regression yielded the most significant results for developing the study model, confirming that personality types and gender differences influence software developers' ability to handle TP. This study offers empirical evidence regarding the impact of TP on humanistic aspects. Furthermore, the model developed can be leveraged to enhance the success rate of software projects in the field of SE

Work safety scale influcing safety behaviour in signaller army

This study investigates the factors influencing safety behaviour in the Signaller Army, focusing on Work Safety Scale (WSS) factors, including co-worker safety, supervisor safety, management safety practices, and satisfaction of safety programs. The primary objectives were to assess the level of safety behaviour, analyse how WSS factors influence safety behaviour, and identify which WSS factors has the strongest impact on safety behaviour. A quantitative survey was conducted, collecting data from 295 military personnel, including Officers and Other Ranks. The data was analyzed using SPSS version 28.0, employing descriptive statistics, Pearson product-moment correlation coefficient analysis, and multiple regression analysis. The results revealed that Signaller Army personnel exhibited a high level of safety behaviour, with a mean score of 3.893. All WSS factors showed a significant positive relationship with safety behaviour. Specifically, co-worker safety (r = 0.552, p-value < 0.001), supervisor safety (r = 0.931, p-value < 0.001), management safety practice (r = 0.923, p-value < 0.001), and satisfaction of safety programs (r = 0.779, p-value < 0.001) were positively correlated with safety behaviour. Multiple regression analysis revealed that supervisor safety (b = 0.456) was the most influential factor, followed by management safety practices (b = 0.410), co-worker safety (b = 0.118), and satisfaction of safety programs (b = 0.091). These findings emphasize the importance of leadership, effective organizational practices, and strong teamwork in promoting safety behaviour within the military. The study offers valuable insights into improving safety culture and behaviour in military contexts, highlighting the need for targeted strategies to enhance safety culture and reduce operational risk

Instructional design model of mobile augmented reality for enhancing comprehension, learning engagement and perceived motivation in students with diverse spatial visualization abilities

Despite various design models that have been proposed and adapted for mobile augmented reality (MAR) applications development, none has addressed the integration of comprehension, learning engagement, and perceived motivation elements in learning Computer System Organization (CSO) among polytechnic students’ students with diverse Spatial Visualization Abilities (SVA). This leaves a critical gap in the development of design models in educational technology for MARbased learning environments. This study aimed to develop and evaluate the Mobile Augmented Reality Instructional Design Model (MARID) for polytechnic students with diverse SVA. Based on the MARID model, two MAR applications were designed and developed; Mobile Augmented Reality Functional Realism (MARCO-FR) and Mobile Augmented Reality Physical Realism (MARCO-PR). Both were designed for the CSO course. A quasi-experimental factorial design was used to examine the effects of the independent variables of MARCO modes, which were the MARCO-FR and MARCO-PR on the dependent variables: comprehension, learning engagement, and perceived motivation, moderated by the students’ SVA. This study involved 200 polytechnic students who were assigned to use one of the MARCO modes. Research procedures included pre-tests, post-tests, expert validation, and alpha-beta testing. The data was analysed using descriptive and inferential statistics which is ANOVA. Findings revealed that students using MARCO-FR demonstrated significantly higher performance in comprehension, learning engagement, and perceived motivation compared to those using MARCO-PR, across both high and low SVA levels. High SVA students showed exceptional improvements in comprehension and learning engagement with MARCO-FR, attributed to its visual realism and emphasis on spatial procedural knowledge. Low SVA students also benefited from both MARCO modes, although their performance gains were more pronounced with MARCO-FR. This study highlights MARID as an effective instructional design model for MAR to address diverse learning needs. MARCO-FR is recommended for students across SVA levels, while MARCO-PR offers potential for broader applications. This research contributes to integrating MAR into instructional design for technical education and advancing educational technolog

The impact of computerized information systems on crisis management in UAE immigration services

The main objective of this study is to investigate the impact of computerized management information systems and their relationship to crisis management in the Passports and Immigration Department of the United Arab Emirates. The problem of the study focused on the challenges encountered in implementing computerized management information systems, specifically in the dimensions of System Quality (SYQ), Service Quality (SEQ), Information System Quality (ISQ), and Decision Making Speed (DMS). The study adopted quantitative approach and benefited from statistical evaluation based on detailed solutions and techniques of variance and covariance, correlation and regression. A questionnaire set was created from previous literary works and modified to match the study conditions. The study population were all employees who are working in the Department of Passports and Immigration of the United Arab Emirates. It was formed according to the Federal Center for Competitiveness and Statistics (9800) employees. The target sample size is 370, but the valid data set for analysis was 330 questionnaires. The results of the main dependent variable Crisis Management (CM) showed significant predictive power. This explained that the independent variables (system quality, system quality services, quality of information, speed of decision-making) in the proposed model accounted for 88% of the changes in crisis management in the Department of Passports and Immigration of the United Arab Emirates. The findings indicated that there was a statistically significant relationship between system quality, quality of services, information quality, and the speed of decision-making in computerized management information systems and crisis management in the Passports and Immigration Department of the UAE. The study recommended conducting similar studies in other places such as other emirates or neighbouring countries. The study also recommended conducting similar studies using a different methodology such as the qualitative approach, which may infer additional variables that may not have been included in the current research

Persepsi risiko,bebanan kerja dan kegembiraan dalam kalangan pekerja perkhidmatan sokongan hospital

This study investigates the relationship between risk perception, workload, and happiness among hospital support service worker. It aims to understand how these factors interact and influence employee well-being and job satisfaction A quantitative approach was employed, involving the distribution of questionnaires to 230 hospital support service workers. The data collected included demographic information and responses to questions about risk perception, workload, and happiness The study found that high perceived risk and excessive workload are associated with increased stress and reduced happiness among workers. However, a moderate workload can enhance happiness by providing a sense of achievement and motivation The research is grounded in the Self-Regulation Theory, which emphasizes the importance of self-control and goal-setting in managing work stress and enhancing job satisfaction The findings suggest that effective workload management and risk perception strategies are crucial for improving employee happiness and productivity. Organizations should focus on creating a balanced work environment to foster positive emotions and job satisfaction This study contributes to the understanding of workplace dynamics in the healthcare sector, providing insights into how risk perception and workload affect employee well-being. It highlights the need for tailored strategies to enhance happiness and performance among hospital support service workers

Transformational leadership, employee engagement, and turnover intention: a case of a textile manufacturing company in Kuala Lumpur

Employee engagement has emerged as a pivotal focus in contemporary management, particularly in sectors like textile manufacturing, where staff retention and productivity are critical for economic success. This study examines the relationship between transformational leadership, employee engagement, and turnover intention at the textile manufacturing company. Transformational leadership, characterized by its ability to inspire and motivate employees, is hypothesized to significantly enhance engagement and reduce turnover intention. A quantitative methodology was employed, utilizing structured questionnaires to collect data from 142 employees at a textile manufacturing company. The data was analyzed using the Statistical Package for the Social Sciences (SPSS), with correlation and regression analyses conducted to explore the relationships among transformational leadership, employee engagement, and turnover intention. The findings reveal that transformational leadership positively influences employee engagement while simultaneously reducing turnover intention. Key leadership behaviors, such as individualized consideration, intellectual stimulation, and inspirational motivation, significantly enhance engagement. However, certain demographic factors, including tenure and age, moderated these relationships. The study suggests that the textile manufacturing company could benefit from adopting transformational leadership practices to foster a more engaged and committed workforce, thereby reducing turnover rates. Future research could expand on these findings by using a larger sample size or exploring the impact of other leadership styles across different industries

Insights into human resources management practices, employee well-being and financial performance within a specific context of the Pakistani telecommunication industry

In the context of Pakistan's telecommunication industry, this study examines the influence of HRM practices on financial performance, with a particular focus on the mediating roles of work intensification and employee well-being, both critical factors in determining employee productivity and satisfaction. The primary objective of this research was to explore the relationship between HRM practices, namely participatory decision-making, supportive management, information sharing, and flexible work, and the financial performance of telecommunication firms in Pakistan. This study adopted a quantitative research design, collecting data through a structured questionnaire distributed to employees of four major telecommunication firms (Jazz, Ufone, PTCL, Zong) operating in the Punjab province of Pakistan. A total of 800 questionnaires were distributed, and 360 valid responses were received. Structural Equation Modeling (SEM) using SmartPLS 4 was employed to analyze the data. The findings revealed that all four HRM practices—participatory decision-making, supportive management, information sharing, and flexible work—had a positive and significant impact on the financial performance of telecommunication firms. Employee well-being was found to significantly mediate the relationship between HRM practices and financial performance, whereas work intensification played a partial mediating role. This study contributes to the HRM literature by providing empirical evidence from the telecommunication sector in a developing economy, Pakistan. The originality of the study lies in its focus on the telecommunication sector and the unique cultural and economic context of Pakistan, offering insights that may be applicable to other industries in similar settings. Practically, it highlights the importance of implementing HRM practices that prioritize employee well-being to enhance financial outcomes. Theoretically, it adds to the growing body of knowledge on the role of mediators like work intensification and employee wellbeing in HRM-performance relationships. However, the study is limited by its focus on one sector and one geographical region, restricting the generalizability of the findings. Future research should explore other sectors and regions, as well as consider longitudinal studies to better understand the long-term effects of HRM practices on financial performanc