Emerging Science Journal (ESJ)
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Impact of Corporate Social Responsibility on the Effectiveness of Companies' Business Activities
Get PDFBackground: Corporate social responsibility (CSR) has a great influence on the sustainability of company development, so it can be considered a business model for business effectiveness. Objective: The objective of the research is to determine the mutual influence of real-estate companies' activities and CSR effectiveness in different countries. This study examines indicators for assessing companies' financial stability, CSR, and working capital management's influence on the activity effectiveness of real-estate companies. Methods/Analysis: Questionnaires, the principal component method, the Sobel test, and linear regression analysis are used to evaluate the relationship between CSR and the business performance of autocratic management-style companies. The authors' algorithm for assessing a company's financial stability, CSR, and capital management, which affect the efficiency of companies, is proposed. Findings: Empirical analysis has shown that management has no mediating effect on CSR and enterprise performance relationships for companies with high financial stability and working capital, though it has a stimulating effect for low financial stability companies. CSR and business performance have positive relationships in companies, but despite financial stability growing, the autocratic leadership style reduces interest in CSR development. This paper conceptualizes the impacts of CSR on the effectiveness of companies. Novelty: The novelty of this study is to create theoretical and practical provisions aimed at laws and regulations. Doi: 10.28991/ESJ-2023-07-03-08 Full Text: PD
A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic
Get PDFThe COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia. Doi: 10.28991/ESJ-2023-SPER-015 Full Text: PD
Perceived Effects of the COVID-19 Pandemic on Loneliness: The Most Vulnerable Population Groups
Get PDFCOVID-19 pandemic lockdown measures reasonably limited the social contacts of people in many countries. It is crucial to understand the effect of such policies on people's social ties and the possible need for evidence-based public policy amendments. Therefore, this study examines 1) the prevalence of loneliness in the population aged 15+ in Lithuania in late 2021 and 2) the self-rated effect of the COVID crisis on loneliness in population groups with different levels of loneliness. It also focuses on the socio-demographic characteristics of these population groups. Data from a representative cross-sectional quantitative survey (N = 1067), carried out in November–December 2021, was used. Based on the 6-item De Jong Gierveld Loneliness Scale, descriptive statistics analysis revealed the high prevalence (51% of a medium level of loneliness) in the Lithuanian population. One in three people (36%) declared low-level loneliness, and each seventh or eighth (13%) reported high-level loneliness. The feelings of respondents who reported a high level of loneliness were also less stable; they more often stated that their feelings of loneliness increased during the pandemic. These research findings make contributions to studies of loneliness within the context of sudden crises. They emphasise the importance of policymakers focusing on additional measures when preparing for future emergencies and providing special attention to residents who experience the highest levels of loneliness. Doi: 10.28991/ESJ-2023-SPER-020 Full Text: PD
On the Locating Rainbow Connection Number of Trees and Regular Bipartite Graphs
Get PDFLocating the rainbow connection number of graphs is a new mathematical concept that combines the concepts of the rainbow vertex coloring and the partition dimension. In this research, we determine the lower and upper bounds of the locating rainbow connection number of a graph and provide the characterization of graphs with the locating rainbow connection number equal to its upper and lower bounds to restrict the upper and lower bounds of the locating rainbow connection number of a graph. We also found the locating rainbow connection number of trees and regular bipartite graphs. The method used in this study is a deductive method that begins with a literature study related to relevant previous research concepts and results, making hypotheses, conducting proofs, and drawing conclusions. This research concludes that only path graphs with orders 2, 3, 4, and complete graphs have a locating rainbow connection number equal to 2 and the order of graph G, respectively. We also showed that the locating rainbow connection number of bipartite regular graphs is in the range of r-⌊n/4⌋+2 to n/2+1, and the locating rainbow connection number of a tree is determined based on the maximum number of pendants or the maximum number of internal vertices. Doi: 10.28991/ESJ-2023-07-04-016 Full Text: PD
E-Learning Adoption: Designing a Network-Based Educational and Methodological Course on "Humans and Their Health"
Get PDFThis study aims to explore the factors influencing the adoption of e-learning platforms in biology education and examine the impact of online learning on students' performance. This study investigates the relationships between perceived usefulness, perceived ease of use, attitude toward e-learning, flexibility, content quality, and students' behavioral intention to adopt e-learning activities. A mixed-methods approach was employed consisting of two phases: a questionnaire survey with structural equation modeling (SEM) to analyze data and an experiment with an independent sample t-test to assess the impact of online learning on student performance. Findings disclosed that perceived usefulness, perceived ease of use, attitude toward e-learning, flexibility, and content quality positively impacted students' behavioral intention to adopt e-learning and their performance. This study contributes to the theoretical understanding of the factors influencing e-learning adoption in biology education. Practical recommendations are provided for educators, instructional designers, and policymakers to facilitate the implementation of e-learning platforms in biology education. These recommendations include promoting the perceived usefulness and ease of use of e-learning platforms, fostering a positive attitude toward e-learning, enhancing flexibility, ensuring high-quality content, providing training and support for educators, and considering the needs of students with disabilities. Doi: 10.28991/ESJ-2023-07-06-014 Full Text: PD
Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods
Get PDFThe development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PD
Collective Action in Institutional Entrepreneurship: The Case of a Government Agency
Get PDFThis paper seeks to analyze how and why divergent institutional changes occurred in a government agency. While there is evidence of research on the concept of collective action and involvement in the literature on institutional entrepreneurship, the focus has been at the macro and field levels, with scarce attention being given to the topic at the micro and organizational levels. This study addresses this gap in the literature, drawing on the institutional entrepreneurship process model of Battilana, Leca, and Boxenbaum (The Academy of Management Annals), in combination with literature on collective action. The methodology involved a longitudinal case study, in which data were collected through extensive interviews and documentation analysis. Based on findings showing that the divergence change process could not be achieved without the support of organizational collective involvement, a refined version of the Battilana et al. entrepreneurial model is proposed. Doi: 10.28991/ESJ-2023-07-02-017 Full Text: PD
New Approach to Image Segmentation: U-Net Convolutional Network for Multiresolution CT Image Lung Segmentation
Get PDFImage processing is the main topic of discussion in the field of computer vision technology. With the increase in the number of images used over time, the types of images with different resolution qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image processing. Therefore, a method with high performance is needed for image processing. In image processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT image dataset, which has different resolutions in each image, to segment the image that produces a segmented lung image. In this study, we conducted experiments for many training and testing data ratios while also comparing the model performances between the single resolution dataset and the multiresolution dataset. The results showed that the segmentation accuracy using a single resolution dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to 1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and 9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio. Doi: 10.28991/ESJ-2023-07-02-014 Full Text: PD
Desktop vs. Headset: A Comparative Study of User Experience and Engagement for Flexibility Exercise in Virtual Reality
Get PDFThis study aimed to investigate the effectiveness of Virtual Reality (VR) technology for flexibility exercise and compare the physical outcomes, user experience, and engagement of VR desktops and VR headsets. The VR exercise application was designed using motion capture technology and exported to different VR devices. Each of the devices was used by 30 participants to perform a flexibility exercise in VR. Physical outcomes were measured using the sit-and-reach test, and user experience and engagement were evaluated using questionnaires and group discussions. The results showed that VR desktop participants had higher sit-and-reach scores. However, VR headset participants reported a more immersive experience (reality judgment) and motivation (value and usefulness). They also had higher engagement (focused attention and reward) levels than VR desktop participants. There were no significant differences between the two approaches in terms of enjoyment, effort, pressure, choice, correspondence, absorption, perceived usability, and aesthetic appeal. The study highlights the importance of considering physical outcomes, user experience, and engagement by comparing two different VR approaches for flexibility exercise. Further research is needed to explore the limitations and potential benefits of VR technology for physical activity. Doi: 10.28991/ESJ-2023-07-04-03 Full Text: PD
An Optimized Machine Learning and Deep Learning Framework for Facial and Masked Facial Recognition
Get PDFIn this study, we aimed to find an optimized approach to improving facial and masked facial recognition using machine learning and deep learning techniques. Prior studies only used a single machine learning model for classification and did not report optimal parameter values. In contrast, we utilized a grid search with hyperparameter tuning and nested cross-validation to achieve better results during the verification phase. We performed experiments on a large dataset of facial images with and without masks. Our findings showed that the SVM model with hyperparameter tuning had the highest accuracy compared to other models, achieving a recognition accuracy of 0.99912. The precision values for recognition without masks and with masks were 0.99925 and 0.98417, respectively. We tested our approach in real-life scenarios and found that it accurately identified masked individuals through facial recognition. Furthermore, our study stands out from others as it incorporates hyperparameter tuning and nested cross-validation during the verification phase to enhance the model's performance, generalization, and robustness while optimizing data utilization. Our optimized approach has potential implications for improving security systems in various domains, including public safety and healthcare. Doi: 10.28991/ESJ-2023-07-04-010 Full Text: PD