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A New Bw Index for Quantifying Scholars' Research Influence
No full textThe growing importance of measuring and evaluating academic performance in academic hiring, promotions, funding allocation, and resource distribution has fueled the demand for better metrics. Traditional ranking indicators, such as publication count and citation-based indices, often fail to capture for the interdisciplinary influence and qualitative dimensions of research impact. These shortcomings highlight the need for more comprehensive evaluation metrics. The current study introduces a novel BW Index, which integrates both quantitative and qualitative aspects of researcher contributions aiming to provide a more balanced and comprehensive evaluation of scholarly impact. For evaluating the effectiveness of proposed index, a comparative analysis was conducted on 200 researchers' profiles of Monash University Australia calculating both the h-index and the proposed BW Index. The results of study indicate that researchers with identical h-index exhibit significant variation in BW Index values ranging from 10 to 55, demonstrating its ability to distinguish research impact beyond citation counts. Furthermore, for researchers with an h-index of 20, the BW Index ranges from 20 to 82, reflecting an increase in differentiation compared to traditional h index. These findings highlight the BW Index as a more nuanced and equitable measure of academic influence, offering a refined approach to researcher evaluation and addresses the limitations of traditional metrics
Sentiment Analysis of the Israel-Palestine Conflict on X: Insights from the Indonesian Perspective using a Long Short-Term Memory Algorithm
No full textThe Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on performance metrics to evaluate the ability of the classification model to distinguish between classes; 5) visualization of experimental results. The LSTM model is a modification of the recurrent neural network (RNN). The LSTM model has many advantages, including being able to remember a collection of information that has been stored for a long period of time, being able to delete information that is no longer relevant, and being more efficient in processing, predicting, and classifying data based on a certain time sequence. Another advantage is that LSTM's ability to identify temporal dependencies and contextual interactions in sequential data makes it suitable for social media text analysis. The model demonstrated success in sentiment classification on geopolitical topics with an impressive accuracy rate of 91%. The findings demonstrate deep learning's potential applications for sentiment analysis and offer insights into public opinion dynamics during times of international crises
Implementation of Lightweight Machine Learning Models for Real-time Text Classification on Resource-Constrained Devices
No full textThis paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge computing and smart devices proliferate, there is an urgent need for more advanced NLP technology that does not require constant cloud access and is efficient in computing and provides results in real time. While deep learning and cloud-based models typically offer high text-classification accuracy and have demonstrated exceptional performance across a range of NLP tasks, they are often too resource-intensive for real-time deployment in constrained environments. To overcome these limitations, we explore a set of lightweight machine learning (ML) models—Multinomial Naive Bayes, Logistic Regression, and Decision Tree—to perform sentiment classification on a subset of the Amazon Reviews Polarity dataset. Following thorough data preprocessing and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, two optimization techniques are employed: feature selection via Chi-Squared tests and simulated post-training quantization. Our experimental results show that resource consumption can be substantially reduced, with minimal accuracy loss, thereby demonstrating feasibility for edge-based text analytics and offline functionality. We provide a detailed comparative analysis that highlights how classical ML models remain viable in scenarios where modern deep learning architectures cannot be efficiently deployed
Vendor Evaluation and Selection for Forwarding Activities Using Stepwise Weight Assessment Analysis-Combined Compromise Solution (SWARA-CoCoSo) Method
No full textPT Perusahaan Listrik Negara Suku Cadang has faced delays in receiving commodities requested by the Perusahaan Listrik Negara Group, resulting in materials arriving later than expected. These materials were supplied by PT Wartsila, a partner of PT Perusahaan Listrik Negara Suku Cadang, responsible for fulfilling the orders placed by the Perusahaan Listrik Negara Group. To uphold its reliability as a supply chain company, PT Perusahaan Listrik Negara Suku Cadang must ensure timely delivery of requested goods. One way to minimize delays is through vendor evaluation. The SWARA method, which assesses ten factors identified by four logistics division experts, is employed to select the best forwarding vendor. The CoCoSo method, along with PT Perusahaan Listrik Negara Suku Cadang's logistics performance evaluation, was used to determine the top vendor. Based on the CoCoSo results, PT Kurnia Purnama Jaya ranked first with a score of 4.06, followed by Mats International Indonesia with a score of 2.6, Perigi Raja Terpadu in third with 1.5, and Pos Logistik Indonesia in fourth with 1.4. According to the CoCoSo method’s criteria weightings and vendor evaluation, PT Kurnia Purnama Jaya was selected as the most suitable vendor for PT Perusahaan Listrik Negara Suku Cadang.
Manuscript received: 19 Sep 2024 | Revised: 12 Dec 2024 | Accepted: 29 Dec 2024 | Published: 31 Mar 202
Review on Advancements in Artificial Intelligence and its Applications in Sports
No full textThe sport industry is being transformed by Artificial Intelligence (AI) in many ways. This paper seeks to discuss how AI has improved sports science, particularly in boosting the athletes’ performance and avoiding injuries, through various machine learning models like Extreme Gradient Boosting, Support Vector Machines, and Random Forest Regression. These AI tools are more effective than the traditional methods, as they predict the athletes’ performance results more accurately and managing their injuries more proactively. This paper also discusses the challenges of using AI in the sport industry, particularly in terms of data privacy and the reliability of the models. With the aid of AI, it is of no doubt that sport science will have a promising future.
Manuscript received: 24 Oct 2024 | Revised: 10 Dec 2024 | Accepted: 17 Dec 2024 | Published: 31 Mar 202
Enhancing LLM Efficiency: A Literature Review of Emerging Prompt Optimization Strategies
No full textThis study focuses on enhancing the performance of Large Language Models (LLMs) through innovative prompt engineering techniques aimed at optimizing outputs without the high computational costs of model fine-tuning or retraining. The primary objective is to investigate efficient alternatives, such as black-box prompt optimization and ontology-based prompt refinement, which improve LLM performance by refining prompts externally while maintaining the model's internal parameters. The study explores various prompt optimization techniques, including instruction-based, role-based, question-answering, and contextual prompting, alongside advanced methods like CoT and ToT prompting. Methodologically, the research involves a comprehensive literature review, benchmarking prompt optimization techniques against existing models using standard datasets such as Big-Bench Hard and GSM8K. The study evaluates the performance of approaches like APE, PromptAgent, self-consistency prompting, and many more. The results demonstrate that these techniques significantly enhance LLM performance, particularly in tasks requiring complex reasoning, multi-step problem-solving, and domain-specific knowledge integration. The findings suggest that prompt engineering is crucial for improving LLM efficiency without excessive resource demands. However, challenges remain in ensuring prompt scalability, transferability, and generalization across different models and tasks. The study highlights the need for further research on integrating ontologies and automated prompt generation to refine LLM precision and adaptability, particularly in low-resource settings. These advancements will be vital for maximizing the utility of LLMs in increasingly complex and diverse applications.
Manuscript received: 3 Oct 2024 | Revised: 13 Dec 2024 | Accepted: 25 Dec 2024 | Published: 31 Mar 202
Effects of Political Connections on Earnings Management Practices in Nigeria: Does the Board of Directors’ Efficacy Matter? DOI: https://doi.org/10.33093/ijomfa.2025.6.1.5
No full textThe purpose of this study is to investigate the effects of political connections and earnings management and to explore the role of the board of directors’ efficacy on the relationship between political connections and earnings management practices. A panel data set of 365 observations from 73 firms (2018 to 2022) listed on the Nigerian Exchange Group (NGX) was used, and the Driscoll and Kraay standard error fixed effect was employed in testing the hypotheses. The findings indicated that politically connected boards are positively associated with accrual earnings management and negatively associated with real earnings management practices. The study also finds that the board of directors’ efficacy is negatively associated with both accrual earnings management and real earnings management activities and thus plays a significant role in strengthening accrual earnings management practices of politically connected boards. The results are robust to alternative accrual earnings management and real earnings management measures. However, following the reformation of the Nigerian Code of Corporate Governance 2018, this study is among the earliest to examine the effects of board efficacy on earnings management of firms with politically connected boards in Nigeria. As such, the findings might have important implications for policymakers, regulators, and investors, as board efficacy is a significant mechanism in strengthening the accrual earnings management practices, thereby curbing the earnings management of politically connected boards. Additionally, this study is limited to a sample of non-financial service firms in Nigeria for a period of 5 years, resulting in the non-generalizability of the findings in different contexts
Review of the Role of Psychological Curriculum in Vocational and Technical Education in China: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.1
No full textThe integration of psychological education in China's vocational and technical education (VTE) schools is increasingly recognized for its role in promoting students' personal and professional growth. This review synthesizes recent research to evaluate the integration of psychological education in VTE schools and its benefits, outcomes, and challenges. A psychological curriculum is found to enhance both students' personal and professional development. However, key findings also highlight the inconsistency in the implementation of psychological education among VTE schools and the insufficiency in updating the curriculum to address students' changes due to the transformation in technology and economy. Proposing a mixed-methods approach, the study employs stratified random sampling to survey 500 VTE students and purposive sampling to conduct semi-structured interviews and focus groups with 30 students. Quantitative data analysis provides insights into students' experiences with psychological education, while qualitative findings offer a deeper understanding of how psychological curricula influence their academic and career development. This research contributes to the existing literature by highlighting the need for a more structured, evidence-based psychological curriculum in VTE institutions
How Personality Shapes Workplace Incivility: A Theoretical View: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.3
No full textWorkplace incivility, characterised by low-intensity, ambiguous behaviour such as disrespect, rudeness, and discourtesy, disrupts workplace harmony and erodes organisational culture. Although these behaviours may appear minor, their cumulative impact can significantly harm individuals and organisations, manifesting in decreased morale, productivity, and well-being. Personality traits play a crucial role in shaping how employees perceive, experience, and respond to workplace incivility, influencing its outcomes and associated costs. However, existing literature offers limited insights into how these personality differences exacerbate experiences of workplace incivility, leaving a critical gap in understanding this phenomenon. This conceptual paper draws on victim precipitation theory and trait activation theory to examine the intricate relationships between agreeableness, neuroticism, conscientiousness, and negative affectivity with workplace incivility. By integrating these theoretical perspectives, this paper proposes a framework to understand how personality traits influence susceptibility to incivility and its subsequent effects on individuals and organisations. This study aims to advance theoretical discourse, guide future empirical research, and inform organisational strategies to mitigate the adverse consequences of workplace incivility
Employee Turnover Intentions: An Empirical Study on Five-Star Hotels in Malaysia: DOI: https://doi.org/10.33093/ijomfa.2025.6.2.6
No full textThis research aims to explore how economic, social, psychological, and training and development factors influence employee turnover intentions in five-star Malaysian hotels. Data was collected through questionnaires and distributed to 120 employees using convenience sampling, and SPSS was used to perform the data analysis. The results of the multiple regression analysis revealed that economic factors (including pay, reward, and benefits), social factors (including co-workers, manager or supervisor, and working environment), and training and development factors (including training opportunity and career development) are significantly related to employee turnover intentions. In contrast, psychological factors (including job involvement, personal characteristics, personal life, and family) are not significantly related. The research provides valuable insights to help build effective retention strategies in five-star hotels in Malaysia by addressing the diverse needs and expectations of the employees