This study designed a big data analytics artefact for the prediction of outcome-based funding (OBF) in South African public universities. Universities in South Africa (SA) are subsidized based on... Show More

This study designed a big data analytics artefact for the prediction of outcome-based funding (OBF) in South African public universities. Universities in South Africa (SA) are subsidized based on their performance known as OBF that is measured depending on the outputs from teaching, research, and engagements. OBF metrics are well documented; however, public universities fail to achieve the targets for higher scores. These failures are attributed to poor decision-making resulting from limited analysis of the voluminous data generated. This study used design science methodology to develop a big data analytics artefact for prediction of OBF outcomes. The artefact was evaluated for prediction using machine learning training and tested with data collected from South African universities. Findings indicated that for better prediction using big data analytics, system characteristics, size, structure, top management support, market, infrastructure, and government regulations factors play a significant role.

This paper uses data envelopment analysis to explore how to evaluate physician and hospital clinical efficiency for coronary artery bypass surgery. First, the DEA models (measuring overall technical... Show More

This paper uses data envelopment analysis to explore how to evaluate physician and hospital clinical efficiency for coronary artery bypass surgery. First, the DEA models (measuring overall technical and scale efficiency partitioned by severity) identified many inefficient surgeons. Regardless of time period studied, relatively few surgeons were found to be on the best-practicing production frontier (DEA efficiency score = 1). The authors offer some evidence that clinical efficiency may be subject to investing in organizational capabilities that arise from operational strategies such as developing open heart surgery as a specialty, years of experience, training, and ultimately, acquiring a cadre of “efficient” surgeons. At a minimum, these findings support including some measures of “superior” organizational capabilities, strategic focus or product specialization, continuing education, and experience in future work.

The influx of international students has moved beyond the Global North towards the Global South in countries like China, Malaysia, Singapore, and Hong Kong as they pursue strategies to become an... Show More

The influx of international students has moved beyond the Global North towards the Global South in countries like China, Malaysia, Singapore, and Hong Kong as they pursue strategies to become an international education hub in Asia. This massive competition among higher education institutions (HEIs) worldwide necessitates revisiting the existing image models to understand their heightened impacts on student satisfaction for both retaining existing students and attracting new ones. This study attempts to do so by gathering responses from 200 international postgraduate students from two renowned universities in Malaysia. The redefined HEI image model based on postgraduate international students shows that all of its three dimensions have a positive significant impact on students' satisfaction and decision-making. The outcomes of importance-performance map analysis (IPMA) have yielded the results of PLS-SEM both at the construct and indicator levels toward building more socially responsible HEIs as a service industry to boost student satisfaction in both regular and uncertain times.

The purpose of this study was to investigate the influence of cultural diversity on job satisfaction and its effect on expatriate work performance. Structured interviews were conducted to collect... Show More

The purpose of this study was to investigate the influence of cultural diversity on job satisfaction and its effect on expatriate work performance. Structured interviews were conducted to collect qualitative data from a sample group of expatriates five participants from mixed nationalities in a private sector organization based in the United Arab Emirates. Thematic analysis identified three emergent themes of culture that negatively impacted expatriates' job satisfaction, causing reduced employee performance, and two themes were identified that increased expatriate performance at work (enhanced creativity and innovation). This research contributes to the body of academic knowledge about the effect of cultural diversity on expatriate employee performance. The value for praxis from this study are recommendations for managerial action to improve expatriate work performance.

The purpose of this study is to investigate the complexities of inventory management in the healthcare industry for improving efficiency and resilience in healthcare supply chains. A Systematic... Show More

The purpose of this study is to investigate the complexities of inventory management in the healthcare industry for improving efficiency and resilience in healthcare supply chains. A Systematic Literature review using Scopus database and PRISMA approach was performed. Driven by innovation and technology, new developments are changing the face of inventory management. Predictive analytics and stock optimization are made possible by the increasing deployment of AI and machine learning. Autonomous Mobile Robots and RFID technology are being used more for quick identification and data collection, while Internet-of-Things devices are used for real-time tracking. Blockchain technology to guarantee supply chain traceability and transparency is also being investigated. Another is automation through robots, which lowers human error and increases warehouse operation efficiency. Finally, the incorporation of cloud-based systems such as SMART logistics and E-logistics platforms enables distant access to inventory information, encouraging adaptability and cooperation across various sites.

Multi-criteria decision making is a complex process that involves considering various criteria or factors when making a decision. Over the past few decades, several researchers have developed... Show More

Multi-criteria decision making is a complex process that involves considering various criteria or factors when making a decision. Over the past few decades, several researchers have developed different methods to tackle this problem. This method offers manifold advantages, however, it faces several limits leading to the development of different modified forms. This paper provides a comprehensive review on the TOPSIS concept to help the researchers gain an overview in different aspects of TOPSIS. The literature review presented in this paper offers a comprehensive survey of the TOPSIS method, providing researchers with an overview of the various aspects of the method, its strengths and limitations, and its various applications. The results of the literature review also are provided based on the application, methodology, and reasons to use TOPSIS method.

The growth of the senior population and the rise in chronic illnesses such as non-communicable diseases (NCDs) are placing significant strain on modern healthcare systems. The demand for resources... Show More

The growth of the senior population and the rise in chronic illnesses such as non-communicable diseases (NCDs) are placing significant strain on modern healthcare systems. The demand for resources from hospital beds to doctors and nurses is high in Upper-Middle-Income countries like Malaysia. The use of IoT technologies can give aged people at home uninterrupted access to healthcare resources and better control over their health. This study aims to analyze the key antecedents of the actual behavior of IoT technologies among aged NCD patients in Malaysia under a framework covering the unified theory of acceptance and use of technology model, technology acceptance model, and theory of planned behavior. This cross-sectional study used a purposive sampling technique and data were analyzed using SmartPLS. The outcome is expected to help the government and healthcare policymakers develop awareness among the senior community on the use of IoT-based smart wearables for routine self-monitoring of NCDs.

Osteoarthritis (OA) is a multifaceted ailment posing challenges in its diagnosis and treatment due to the intricate nature of the disease. Particularly, Knee Osteoarthritis (KOA) significantly... Show More

Osteoarthritis (OA) is a multifaceted ailment posing challenges in its diagnosis and treatment due to the intricate nature of the disease. Particularly, Knee Osteoarthritis (KOA) significantly impacts the knee joint, manifesting through symptoms such as pain, stiffness, and limited movement. Despite its prevalence and debilitating effects, early detection of KOA remains elusive, often hindered by subjective diagnostic methods and the absence of reliable biomarkers. This research aims to address these challenges by leveraging deep learning techniques and ensemble methodologies for accurate KOA classification using knee X-ray images. This paper utilized a dataset sourced from the Osteoarthritis Initiative (OAI), comprising a large collection of knee X-ray images graded according to the Kellgren-Lawrence (KL) grading system. The proposed design methodology involves preprocessing the input X-ray images and training multiple pre-trained Convolutional Neural Network (CNN) models, including ResNet50, InceptionResNetV2, and Xception to classify KOA severity grades. Additionally, this work introduced an ensemble model by combining predictions from these base models to improve overall performance of the Computer-Aided Diagnosis (CAD) system. The obtained results demonstrate the effectiveness of the ensemble approach, outperforming individual algorithms in terms of accuracy, precision, recall, F1-score, and balanced accuracy. However, challenges persist in accurately distinguishing between adjacent KL grades, particularly grades#1 and #2, highlighting the need for further refinement. Notably, the proposed CAD model showcases superior predictive accuracy compared to various state-of-art methods, offering a promising avenue for early KOA diagnosis and personalized treatment strategies.

Identification of diseases from body scans requires design of pre-processing, filtering, segmentation, feature representation, classification & post-processing operations. Existing deep... Show More

Identification of diseases from body scans requires design of pre-processing, filtering, segmentation, feature representation, classification & post-processing operations. Existing deep learning-based classification models use context-specific segmentation followed by convolutions or transformer-based classification techniques. However, the majority of these models do not perform correlative analysis between different disease types. This limitation restricts their scalability and applicability in clinical scenarios. To address these limitations, this research work proposed IMAC-MOC, a model that integrates multimodal augmentations to enhance performance in cross-domain and multi-organ image classification. The proposed model initially collects large-scale scans at organ-level like MRI, CT scan, Kidney Scans, etc. and represents them via multimodal feature sets. These feature maps are combined and processed via a Bacterial Foraging Optimizer (BFO) which assists in identification of high variance inter-class feature sets.