IJHEM 2054-9849 2054-9857 https://doi.org/10.24052/IJHEM/ 21 Volume 11 Issue 02 2025-08-01

108 https://ijhem.com/details&cid=108 2025-09-15 In today’s dynamic business environment, Machine Learning (ML) or algorithm-based, data-driven models are essential for competitive advantage and strategic planning. This study aims to demonstrate the effectiveness of ML models - specifically the standard K-means clustering algorithm in identifying patterns that can inform strategic business decisions. A synthetic dataset was generated to simulate real-world business data scenarios, and the K-means algorithm was applied both with and without data pre-processing techniques such as scaling. The results indicate that although K-means remains a powerful and widely applicable clustering method, its performance is significantly improved by proper data scaling and identification of the optimal number of clusters. The findings of this study offer valuable insight how to develop business strategies over complex business scenarios. Annas, M. and Wahab, S. N., 2023. Data mining methods: K-means clustering algorithms. International Journal of Cyber and IT service management, 3(1). ISSN: 2797-1325. Arthur, D. and Vassilvitskii, S., 2007. K-Means++: The advantage of careful seeding in Proc. 18th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, Louisiana, USA. Bereta, L., Cohen-Addad, V., Lattanzi, S. and Parotsidis, N., 2023. Multi-swap K-means++ in Proc. of the 37th International Conference on Neural information processing Systems, Article no: 1135. pp. 26069-26091. New Orleans LA USA. Bishop, C. M. and Bishop, H., 2023. Deep Learning: Foundations and Concepts. Springer. Cham, Switzerland. Dalmaijer, E. S., Nord, C. L and Astle, D. E., 2022. Statistical power for cluster analysis. BMC Bioinformatics, vol. 23. Article number 205. Frost, N., Moshkovitz, M and Rashtchian, C., 2020. ExKMC: Expanding Explainable k-Means clustering. arXiv preprint arXiv:2006.02399. Available at: https://arxiv.org/abs/2006.02399  Hastie, T., Tibshirani, R. and Friedman, J., 2009. The Elements of Statistical Learning: Data mining, Inference, and Prediction. Springer. New York, NY. Herdiana, I., Kamal, M. A., Triyani, Estri, M. N. and Renny, 2025. A more precise elbow method for optimum K-means clustering. arXiv preprint arXiv:2502.00851. Available at: https://arxiv.org/abs/2502.00851 Hossain, M. Z., Akhtar, M. N., Ahmad, R.B., and Rahman, M., 2019. A dynamic K-Means clustering for data mining. Indonesian Journal of Electrical Engineering and Computer Science, 13(2). pp. 521 – 526. Husein, A.M., Waruwu, F.K., Batu Bara, Y.M.T., Donpril, M. and others, 2021. Clustering Algorithm For Determining Marketing Targets Based on Customer Purchase Patterns And Behaviors. Sinkron: Jurnal dan Penelitian Teknik Informatika, 6(1), pp.137–143. Available at: https://www.researchgate.net/publication/355591874_Clustering_Algorithm_For_Determining_Marketing_Targets_Based_Customer_Purchase_Patterns_And_Behaviors Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., and Heming, J., 2023. K-means clustering algorithms: A comprehensive review, variants, and advances in the era of big data, Scientific Reports, vol. 622, April 2023, pp. 178-210. https://www.nature.com/articles/s41598-023-33214-y John, J.M., Shobayo, O. and Ogunleye, B., 2024. An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market. arXiv preprint arXiv:2402.04103. Available at: https://arxiv.org/abs/2402.04103 Lam, D. and Wunsch, D., 2014. Clustering. Signal Processing: Signal processing Theory and Machine Learning, Signal Processing Theory and Machine Learning, vol. 1, pp. 1115-1149. https://www.researchgate.net/publication/285180280_Clustering, DOI: DOI:10.1016/B978-0-12-396502-8.00020-6 Lang, A. and Schubert, E., 2024. Accelerating K-Means clustering with Cover Trees* in Proc. of International Conference on Similarity and Search Applications (SISAP 2023). Lecture Notes in Computer Science (LNCS, Volume 14289). Lattanzi, S. and Soler, C., 2019. A better K-means++ algorithm via local search in Proc. of the 36th International Conference on Machine Learning, PMLR97. Volume 97, pp. 3662—3671. Found at https://proceedings.mlr.press/v97/lattanzi19a.html. San Diego CA, USA. Lee, S. S. and Lin, J. C., 2012. An accelerated K-means clustering algorithm using selection and erasure rules. Journal of Zhejiang university SCIENCE C, vol. 13, pp. 761-768. Springer nature link found at https://doi.org/10.1631/jzus.C1200078. Li, Y. and Wu, H., 2012. A clustering method based on K-Means algorithm, 2012. International Conference on Solid State Devices and Materials Science. Physics Procedia, vol. 25 (2012), pp.1104 – 1109. Manish, S. and Sanjay, S., 2024. A review on analysis of K-Means clustering machine learning algorithm based on unsupervised learning. Journal of Artificial Intelligence and Systems, vol. 6, pp. 85-95. ISSN: 2642-2859. Mnih, V., Kavukcuoglu, K., Silver, D, and Rusu, A. A., 2015. Human-level control through deep reinforcement learning. Nature, 518(7540), pp. 529-533. DOI: 10.1038/nature14236 https://pubmed.ncbi.nlm.nih.gov/25719670/ Mohammadi, S. O., Kalhor, A., and Bodaghi, H., 2021. H-Splits: Improved K-Means clustering algorithm to automatically detect the number of clusters. Computer Vision and Pattern Recognition. University of Tehran, Iran. Napolean, D. and Pavalakodi, S., 2011. A new method for dimensionality reduction using K-Means clustering algorithm for high dimensional data set. International Journal of Computer Applications, 13(7). DOI: 10.5120/1789-2471 https://www.ijcaonline.org/archives/volume13/number7/1789-2471/ Oti, E. U., Olusola, M. O., Eze, F. C. and Enogwe, S. U., 2021. Comprehensive Review of K-Means Clustering Algorithms, International Journal of Advances in scientific Research and Engineering, 7(8). pp. 64—69. E-ISSN: 2454-8006. DOI: 10.31695/IJASRE.2021.34050 Pasin, O. and Gonenc, S., 2023. An investigation into epidemiological situations of COVID with fuzzy K-means and K-prototype clustering methods. Scientific Reports, vol. 13. Article number 6255.  https://www.nature.com/articles/s41598-023-33214-y. Sinaga, K. P. and Yang, M. S., 2020. Unsupervised K-Means Clustering Algorithm. IEEE Access, vol. 10, pp. 80716 – 80727, ISSN: 2169-3536. DOI: 10.1109/ACCESS.2020.2988796 https://www.scirp.org/reference/referencespapers?referenceid=3464953 Singh, S. and Gill, N. S., 2013. Analysis and study of K-means clustering algorithm. International Journal of Engineering Research and Technology, 2(7). ISSN: 2278-0181. Sutton, R. S. and Barto, A. G., 2018. Reinforcement Learning: An introduction. MIT Press. Second Edition. Cambridge, MA. http://incompleteideas.net/book/the-book-2nd.html Suyal, M. and Sharma, S., 2024. A review on analysis of K-Means clustering machine learning algorithm based on unsupervised learning. Journal of Artificial Intelligence and Systems, vol. 6, pp. 85-95. ISSN: 2642-2859. Wongoutong, C., 2024. The impact of neglecting features scaling in K-means clustering. PLoS One, 19(12). Mae Fah Luang University, Thailand. https://pmc.ncbi.nlm.nih.gov/articles/PMC11623793/. Xiao, B., Wang, Z., Liu, Q. and Liu, X., 2018. SMK-means: an improved mini batch K-means algorithm based on mapreduce with big data. Tech Science Press, 1(1), pp. 1-5. Xie, W., Wang, X. and Xu, B., 2020. An improved K-means clustering algorithm based on density selection in Proc. of the 2020 international Conference on Machine Learning and big data Analytics for IoT Security and Privacy, 2. Shanghai, China November 2020. Yu, l., 2024. The application of K-means clustering algorithm in the evaluation of e-commerce websites. J.  Electrical Systems, 20(6). Zhu, X. and Goldberg, A. B., 2009. Introduction to Semi-Supervised Learning. Morgan and Claypool Publishers. Series: Synthesis Lectures on Artificial Intelligence and Machine Learning , ISBN: 978-1-59829-547-4. San Rafael, CA. Zubair, M., Iqbal, M. A., Shil, A., Chowdhury, M. J. M., Moni, M. A. and Sarkar, I. H., 2024. An improved K-means clustering algorithm towards an efficient data-driven modelling. Annals of Data Science (2024), 11(5), pp. 1525 – 1544. https://ijhem.com/cdn/article_file/2025-09-15-22-00-34-PM.pdf Md Mizanur Rahman Palto Datta Business Strategies Competitive Advantages Data-driven K-means Algorithm K-means Algorithm Pre-processing Techniques Strategic Planning 2270 19 0

109 https://ijhem.com/details&cid=109 2025-10-05 Emotional Intelligence is a vital strategy that should be emphasized in both public and private higher education institutions as it involves recognizing emotions as valuable sources of information that support effective interaction within social contexts. EI encompasses the ability to control one’s own emotions, understand other feelings, and influence through processes and behaviors accordingly. Integrating EI into university settings can improve students’ cognitive approaches not only through academic theories but also through engaging them in practical activities such as economic simulation models and consultation circles. These methods foster the extraction of economic insights that can inform policymakers’ decisions.  Simulation models significantly influence both students and policymakers. However, their current use is largely centered on improving students’ knowledge, skills, and overall character development. While this educational benefit is widely recognized, the potential impact of these models on policymakers is overlooked. It is essential to acknowledge that policy makers can benefit greatly from engaging with students through consultation circles, which provide innovative ideas, and collaborative economic solutions. This paper argues for the relationship between economic simulation models developed by academic advisors in public and private institutions as tools of emotional intelligence and their role in promoting economic integration. This study aims to explore student-led economic simulation models in universities by examining their effectiveness, practices, activities, and outcomes. Using qualitative methods specifically interviews and surveys with university students and academic advisors, this study aims to determine whether the evidence supports or rejects the relationship between both variables Busch, C. (2014) ECGBL2014-8th European Conference on Games Based Learning: ECGBL2014, Google Books. Available at: https://books.google.com/books/about/ECGBL2014_8th_European_Conference_on_ Gam.html?id=IedEBQAAQBAJ (Accessed: 09 July 2025). Campos, N. et al. (2020) Simulation-based education involving online and on-campus models in different European Universities - International Journal of Educational Technology in higher education, SpringerOpen. Available at: https://doi.org/10.1186/s41239-020-0181-y (Accessed: 09 July 2025). Doi name 10.1109 values (no date a). Available at: https://doi.org/10.1109 (Accessed: 09 July 2025). Dr. Joanna Petrides PSY.D., NPI 1821496936 - psychologist in Stratford, NJ (no date). Available at: https://npiprofile.com/npi/1821496936 (Accessed: 09 July 2025). Emotional intelligence - Peter Salovey, John D. Mayer, 1990 (no date a). Available at: https://doi.org/10.2190/DUGG-P24E-52WK-6CDG (Accessed: 09 July 2025). Foundations of game-based learning (no date a). Available at: https://files.eric.ed.gov/ fulltext/EJ1090277.pdf (Accessed: 09 July 2025). Ghaffarzadegan, N. (no date) Simulation-based what-if analysis for controlling the spread of covid-19 in universities, PLOS ONE. Available at: https://doi.org/10.1371/journal.pone.0246323 (Accessed: 09 July 2025). Informs (no date a). Available at: https://pubsonline.informs.org/doi/pdf/10.1287/ited.2019.0227 (Accessed: 09 July 2025). Kurdi, S.F. and Hamdy, N.A. (2020) Emotional intelligence: What do we know and what is new? A review., Minia Journal of Medical Research. Available at: https://doi.org/10.21608/mjmr.2022.221099 (Accessed: 09 July 2025). O’Neil, J. (1995) On emotional intelligence: A conversation with Daniel Goleman., Educational Leadership. Available at: https://eric.ed.gov/? q=intelligence%2BAND% 2Bemotional% 2BAND%2BDaniel%2BAND%2BGoleman&pg=2&id=EJ530623 (Accessed: 09 July 2025). Pejić Bach, M. et al. (2020) Usage of simulation games in Higher Educational Institutions Teaching Economics and Business, ENTRENOVA - ENTerprise REsearch InNOVAtion. Available at: https://hrcak.srce.hr/250935 (Accessed: 09 July 2025). Silva, R.J.R. da, Rodrigues, R.G. and Leal, C.T.P. (2019) Gamification in management education: A systematic literature review, BAR - Brazilian Administration Review. Available at: https://doi.org/10.1590/1807-7692bar2019180103 (Accessed: 09 July 2025). https://ijhem.com/cdn/article_file/2025-10-05-16-11-52-PM.pdf Lara AbdelRazik Economic Integration Emotional Intelligence Private Universities Public Universities Simulation Models Teaching 1239 14 0

110 https://ijhem.com/details&cid=110 2025-10-05 The active version of post-independence National Education Policy was unveiled by the Government of India on July 09, 2020.The foremost objective of the newest version of the education policy is to endorse holistic education across the country. The policy aims to bridge the gap between classroom teachings and industry requirements through integration of various skill development courses, entrepreneurship education and multidisciplinary courses. The policy also emphasizes exposure of innovation and creativity into the mind-set of the students. The early exposure to entrepreneurial education, skill based education will make the student job-ready and hopes to fulfil the vision of ‘Atmanirbhar-Bharat’. The introduction of the vocational education, multidisciplinary courses, industry academia linkage will make the Indian youth more employable ready than just being a mere graduate. The introduction of Academic Bank of Credit, multiple entry –exit system, will act as a boon to students. The policy aims to transform the previous version of exam oriented evaluation system to more value oriented evaluation system. The study aspires to find out, how the modern version of the education policy will effortlessly transform the classroom learnings in to career building. Amrutrao, S. A. Breaking Barriers: NEP 2020's Potential to Transform Professional Education and Industry Placement in India. Maharashtra State Commerce Association, 14. Bag, A., & Chattopadhyay, K. (2024). Women’s empowerment through vocational and technical education (vte): opportunities, challenges and societal impact. vidya-a journal of gujarat university, 3(2), 145-151. Corner, S. (2009). Choosing the right type of rotation in PCA and EFA. JALT testing & evaluation SIG newsletter, 13(3), 20-25. Gupta, S. Entrepreneurship Opportunities for Women: NEP 2020 and Startup Ecosystem. Harwood, T. G., & Garry, T. (2003). An overview of content analysis. The marketing review, 3(4), 479-498. Kalyani, P. (2020). An empirical study on NEP 2020 [National Education Policy] with special reference to the future of Indian education system and its effects on the Stakeholders. Journal of Management Engineering and Information Technology, 7(5), 1-17. Kumar, A. (2021). New education policy (NEP) 2020: A roadmap for India 2.0. University of South Florida (USF) M3 Publishing, 3(2021), 36. Paschal, S., & Srivastav, N. (2022). NEP 2020–Narrowing the Skills Gap and Equipping New Skills through Job Satisfaction. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 2(1). Shukla, B., Joshi, M., Sujatha, R., Beena, T., & Kumar, H. (2022). Demystifying Approaches of Holistic and Multidisciplinary Education for Diverse Career Opportunities: NEP 2020. Indian Journal of Science and Technology, 15(14), 603-607. Singh, K. J., & Gunasekaran, V. (2024). Vocational Education In India: A Policy Analysis And Case Study Of NEP 2020 Implementation. Baileo: Jurnal Sosial Humaniora, 2(1), 76-85. Stapleton, C. D. (1997). Basic Concepts in Exploratory Factor Analysis (EFA) as a Tool To Evaluate Score Validity: A Right-Brained Approach. Stemler, S. E. (2015). Content analysis. Emerging trends in the social and behavioral sciences: An Interdisciplinary, Searchable, and Linkable Resource, 1, 1-14. Sundaram, K. M. (2020). A Study on National Education Policy 2020 Concerning Career Opportunities. Shanlax International Journal of Economics, 9(1), 63-67. Trend, A. S. F. Inspire inclusion: aspiring spirit of women in business through nep 2020. university institute of laws bulletin, 1. https://ijhem.com/cdn/article_file/2025-10-05-16-31-36-PM.pdf Pranam Dhar Debnarayan Saha Pritam Dhara NEP 2020 Entrepreneurship education Vocational education Atmanirbhar Bharat Skill India etc. 1069 10 0

111 https://ijhem.com/details&cid=111 2025-10-05 This paper pushes the conversation about authentic leadership by introducing the variable of identity. Recent discourse on authentic leadership identifies it as an effective and desirable approach to leadership, but to date the conversation has failed to acknowledge that some in leadership are more able to pursue authenticity given their privileged identities, and others in leadership face more risk in authenticity given their minoritized identities. Using key understandings of privilege and authenticity, along with personal testimonies, we provide recommendations for both those with privileged statuses and those with minoritized statuses on how to effectively embrace and employ authentic leadership. Ahmed, S. (2012). On being included: Racism and diversity in institutional life. Duke University Press. Anzaldúa G. (1987). Borderlands/La frontera: The new mestiza. Aunt Lute Books. American Association of University Professors. (2023). 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112 https://ijhem.com/details&cid=112 2025-10-05 This paper explores how Artificial Intelligence (AI) is transforming curriculum development and teaching and learning practices in postgraduate education. Drawing on a multi-case qualitative research design across six global higher education institutions, the study investigates the mechanisms, implications, and challenges of AI adoption at the postgraduate level. The paper is underpinned by a robust conceptual framework that integrates the Technological Pedagogical Content Knowledge (TPACK) model, transformative learning theory, and collaborative intelligence. The findings suggest that AI enables responsive curriculum design, fosters collaborative knowledge creation, and reconfigures the roles of educators. At the same time, the study highlights significant disparities in institutional readiness, ethical ambiguities, and concerns about cognitive outsourcing and student originality. Through interviews with curriculum leaders, academic staff, and policy advisors, and triangulated with institutional strategy documents and AI implementation frameworks, the research provides a multidimensional view of current practices. The paper offers both conceptual contributions and practical implications. It argues that AI can catalyse curriculum innovation when aligned with educational values and pedagogical intentionality. The study concludes with recommendations for curriculum designers, educators, institutional leaders, and policymakers to ethically and effectively integrate AI into postgraduate programmes. This research contributes to a growing body of knowledge calling for critical engagement with AI in higher education, especially at the postgraduate level. Barnett, R. (2011). Being a University. Routledge. Barnett, R., & Coate, K. (2005). Engaging the Curriculum in Higher Education. Open University Press. Biesta, G. (2015). What is education for? 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The State of Higher Education: One Year into the COVID-19 Pandemic. OECD Publishing. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press. Selwyn, N. (2021). The EdTech Tragedy: Critical perspectives on education technology. Learning, Media and Technology, 46(1), 1–13. Shore, C., & Wright, S. (2015). Governing by numbers: Audit culture, rankings and the new world order. Social Anthropology, 23(1), 22–28. Slaughter, S., & Rhoades, G. (2004). Academic Capitalism and the New Economy: Markets, State, and Higher Education. Johns Hopkins University Press Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. West, D., Heath, D., & Huijser, H. (2020). Let’s talk about engagement: Theories and models that shape learning and teaching. Student Success, 11(2), 1–14. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learnin https://ijhem.com/cdn/article_file/2025-10-05-16-56-54-PM.pdf Evangelia Fragouli curriculum development PG students teaching & learning higher education 1221 12 0