journals

Trends in Digital Transformation and Marketing Productivity

Author(s): Riza Akbar*, Retno Widowati
Volume: 5 Issue: 4 Pages: 266-282
DOI: https://doi.org/10.47857/irjms.2024.05i04.01608


Abstract Implementing digital transformation (DT) has become an essential factor in preserving competitiveness and achieving transformative leadership for enterprises. This research examines trends in digital transformation and evaluates the effectiveness of marketing strategies, focusing on the impact of digital technologies such the Internet of Things (IoT), big data, machine learning (ML), and artificial intelligence (AI). […]

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Impact, Challenges, and Prospects of Networking on Social Entrepreneurship in Pakistan

Author(s): Shahzad Ismail*
Volume: 5 Issue: 4 Pages: 252-265
DOI: https://doi.org/10.47857/irjms.2024.05i04.01573


Abstract Social entrepreneurship plays a pivotal role in addressing societal challenges and promoting sustainable development, particularly in Pakistan, where it holds significant potential for economic empowerment, social protection, poverty alleviation, and economic growth. Despite this potential, the full impact of social entrepreneurship is often limited by a lack of understanding and inadequate networking. This study

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ASEAN Engagement’s Impact on Academics and Self-Esteem

Author(s): Joseph A Villarama, Bench G Fabros*, Angelica T Dela Cruz, Ria-Flor V Valdoz
Volume: 5 Issue: 4 Pages: 242-251
DOI: https://doi.org/10.47857/irjms.2024.05i04.01550


Abstract Academic institutions occupy pivotal role in motivating students to engage with Association of Southeast Asian Nations (ASEAN)-relevant and international programmes equipping them to thrive in world’s challenges and transcend transnational boundaries. Using 4-point Likert scale with 60-item researcher-made validated questionnaire, this research measured the level of students’ self-esteem, academic experience, and motivation on ASEAN-relevant

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Healthcare Customers’ Intention to Adopt AI Technologies: A Systematic Literature Review and Future Research Directions

Author(s): Joshy Mathew*, Akansha Mer, Vimlesh Tanwar
Volume: 5 Issue: 4 Pages: 224-241
DOI: https://doi.org/10.47857/irjms.2024.05i04.01507


Abstract This systematic literature review (SLR) explores the factors determining healthcare customers’ intention to adopt artificial intelligence (AI) technologies. Various established theoretical frameworks such as TAM, TAM, UTAUT, BRT, and BHM and their predictors are analysed to comprehend the readiness of healthcare customers to embrace AI-powered technologies in their daily lives. Adhering to PRISMA guidelines,

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Millennial Employee Performance Research Trends

Author(s): Agifian Hanif Firdaus*, Nuryakin, Ika Nurul Qamari
Volume: 5 Issue: 4 Pages: 214-223
DOI: https://doi.org/10.47857/irjms.2024.05i04.01471


Abstract Generation Millennials or generations are also known as Generation Y or Generation Digital natives, defined as individuals born between 1981 and 1996 when Generation Y or Millennials had Already almost entirely entered age. This article uses bibliometric analysis in processing the data, so it is included in meta-analysis research with qualitative data. These bibliometric

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Assessing the Effectiveness of Implied Volatility in Predicting Realised Return Volatility for Informative Decision-Making: Insights from the Nifty Bank Index

Author(s): Sam S, Sathish Pachiyappan*, Saravanan Vellaiyan, Deepika SR, John Paul Raj V
Volume: 5 Issue: 4 Pages: 568-578
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01153


Abstract Implied volatility (IV) is crucial in option pricing models and serves as an essential tool for volatility traders to make informed decisions. However, its effectiveness in predicting realized return volatility is still debated. This study investigates the efficiency of implied volatility in forecasting realized return volatility in the Indian financial markets, specifically using Bank

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Evaluation of Unilateral Transmigration of Cuspid Tooth in Mandibular Arch using 3D Imaging

Author(s): Kushdeep Kumar Gupta, Hemant Sawhney*, Mohd Faisal, Athira Sasikumar, Ritik Kashwani
Volume: 5 Issue: 4 Pages: 661-667
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01206


Abstract Transmigration, where an impacted tooth crosses the midline, is a rare anomaly occurring in less than 1% of cases. Described initially by Nodine and Thoma and later termed by Ando et al. in 1964, it is more common in the mandibular arch and linked with malocclusion, impacted teeth, over-retained primary teeth, and supernumerary teeth.

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Mapping the Landscape of Financial Mindfulness Research: A Bibliometric Study

Author(s): Manjusha J*, Lakshmi Bhooshetty
Volume: 5 Issue: 4 Pages: 554-567
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01147


Abstract Financial mindfulness is an emerging concept in personal finance that promotes a transformative mindset to encourage rational decision-making and mitigate emotional biases. This study explores the evolution of research on financial mindfulness through a systematic literature review and bibliometric analysis of 58 relevant articles. The bibliometric analysis reveals the developmental trajectory of this literature,

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An Effective Route Tracing Using Enhanced Ant Colony Optimization Techniques: Travelling Salesman Problem

Author(s): S Selvi*, D Dhinoovika, R Harshana, R Muthulakshmi
Volume: 5 Issue: 4 Pages: 542-553
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01134


Abstract The Travelling Salesman Problem (TSP) is a well-known optimization that determines the shortest route that visits a particular set of towns and returns to the start line. As an NP-hard, its complexity will increase considerably as the number of cities increases. Several heuristic algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO),

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Metaheuristic Machine Learning Algorithms for Liver Disease Prediction

Author(s): Deepti Gupta, B Kezia Rani, Indu Verma, Shaik Khaleel Ahamed, Arul Mary Rexy V, N Rajkumar, RG Vidhya*
Volume: 5 Issue: 4 Pages: 651-660
DOI: https://doi.org/10.47857/irjms.2024.v05i04.01204


Abstract In machine learning, optimizing solutions is critical for improving performance. This study explores the use of metaheuristic algorithms to enhance key processes such as hyperparameter tuning, feature selection, and model optimization. Specifically, we integrate the Artificial Bee Colony (ABC) algorithm with Random Forest and Decision Tree models to improve the accuracy and efficiency of

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