Mayas Publications logo
Buy Books Buy journals

Journal Issues

Back To Archive
Parental and Related Factors Affecting Students’ Academic Achievement
Dr. S. Prabha Arockia Joans Assistant Professor of Commerce, Holy Cross College, (Autonomous), Tiruchirappalli-620 002.
Pages: 1-10 | First Published: 05 Oct 2025
Full text | Abstract | PDF | References | Request permissions

Abstract

            Numerous components impact the educational outcomes of students. Several of these have been studied by researchers, with many emphasizing the roles of students, schools, governments, peer groups, and others. Often, some of these factors affecting academic achievement are traced back to parents and family—being the primary stage where learning not only begins but is also nurtured, encouraged, and developed, which later influences students’ performance. This study not only explores parental and related factors that predict academic achievement through a review of relevant literature but also examines the impact of parental background on the academic performance of senior secondary school students. As one of the criteria of education quality, students’ academic achievement was analyzed since it is most frequently cited as an indicator of school effectiveness by educationists and school administrators. Data collection was carried out through interviews and well-structured questionnaires administered to one hundred (100) students within the target local government area. The statistical analysis revealed that parents’ attitudes toward their children’s education had a significant impact on students’ self-reported academic achievement. However, factors such as parental education and socio-economic background showed no significant relationship with students’ self-reported academic performance.

Keywords – Parental factors, students

REFERENCES

  1. Paculanet al. (2019) studied the influence of family on the academic motivation of the children. 
  2. Newton and Butler (2019) investigated the impact of temperature in the classroom on students’ performance,      academic motivation, and regularity of attendance.
  3. Jain and Mohta (2019) studied the influence of home environment on the academic performance of the students.
  4. Gosain (2018) In her study examined the correlation between academic motivation and home environment among school students.
  5. Misra (1989) Home environment was measured using the Home Environment Inventory.
  6. Singh & Gupta (1984) Academic anxiety scale, developed was used to measure the academic anxiety of the students. 
A Study on Purchase Intention of Car by Digital Marketing with Reference to Chennai.
S. Ramshankar, Ph.D Research Scholar, (Full Time): Dr. R. Arumugam, Assistant Professor& Research Advisor, PG & Research Department of Management, Maruthupandiyar College, Thanjavur. (Affiliated to Bharathidasan University, Tiruchirapalli.)
Pages: 11-16 | First Published: 06 Oct 2025
Full text | Abstract | PDF | References | Request permissions

Abstract 

Digital marketing is the paramount importance for buying dream cars in online is the current trend to the peoples. Now a days a greater number of Digital marketing techniques are available for all business needs to choose from based on their preference. This digital marketing provides excellent boundary between the car manufacturers and their beloved consumers. The usage of digital marketing tools is a admiring to attain competitive advantage for a business. This study is an earnest effort to investigate the choice of appropriate digital marketing that would create brand awareness among prospective consumers. This study used both primary and secondary data for the study and the sample size is 120 by the simple random sampling the respondents of the study is the dream car buyers of Chennai city. The statistical method used is Correlation amalysis. 

Keywords: Digital Marketing digital content, Purchase Intention. 

References

  1. Gowri, B., & Ramakrishnan, V. (2024). A Study on Understanding Consumer Behavior and Choice Strategies for Automobiles in Thanjavur. Educational Administration: Theory & Practice, 30(5), 7057-7064. 

  2. Prabaharan, M., Selvalakshmi, M., & Jeya Nithilia, R. C. (2024). The Role of Digital Touchpoints in Car Purchasing – An Empirical Research Concerning the Indian Market. Indian Journal of Science and Technology, 17(29), 3044–3053.

  3. Sharma, A. & Kalla, N. (2024). Exploring The Role of Digital Marketing in Shaping Consumer Buying Decisions: Evidence from India’s Passenger Car Market. Educational Administration: Theory & Practice, 30(2), 1988– 1994.   
    Dahiya, R. (2021). Discriminant analysis application to understand the usage of online research by car buyers in India. SAJM 2(1), pp 51-64. 

  4. Yixuan Zhong (2023). “Adoption of Social Media Marketing Strategies in Automotive Industry”. Journal of Education, Humanities and Social Sciences, 16 (July), 123. 

  5. Sreeja J & Naveen Kumar V (2024). “An empirical study on customer preference of Toyota customers (Coimbatore Region)”. IRJMETS (Issue 10, October 2024, Paper 62354).

Artificial Intelligence, Talent Management, and Faculty Outcomes: A Mediated Framework for Educational Excellence a Conceptual Framework
Dr Roomi Rani. Assistant Professor, Govt. SPMR College of Commerce, Jammu (J&K),
Pages: 17-29 | First Published: 05 Oct 2025
Full text | Abstract | PDF | References | Request permissions

Abstract

            In the digital age, Artificial Intelligence (AI) is revolutionizing traditional TM practices by offering predictive, personalized, and data-driven solutions. The rapid integration of Artificial Intelligence (AI) into educational systems has opened new avenues for strengthening institutional performance and academic excellence. The education sector thrives on human capital, where effective Talent Management (TM) ensures academic quality and institutional reputation.  While prior research has predominantly emphasized student-centered applications of AI, limited attention has been paid to the perceptions of faculty and their outcomes in relation to AI-driven Talent Management (TM) practices. Drawing upon existing literature, four key propositions are formulated: (1) AI positively influences TM practices; (2) AI positively influences faculty outcomes; (3) TM practices positively influence faculty outcomes; and (4) AI-supported TM practices positively influence faculty outcomes through mediation. The proposed framework highlights how AI-enabled recruitment, development, engagement, and retention practices can improve faculty performance, productivity, motivation, commitment, job satisfaction, and workforce upskilling. The study contributes to both theory and practice by demonstrating how AI-enabled TM practices can foster sustainable faculty outcomes, ultimately advancing educational excellence in the new era of digital transformation. Implications for policymakers and academic leaders are discussed, alongside recommendations for leveraging AI responsibly to align institutional strategy with faculty growth and institutional sustainability.

Keywords: Artificial Intelligence, Talent Management, Faculty Outcomes, Higher Education, Educational Excellence, Mediated Framework

References

  1. Abdullahi, M. S., Adeiza, A., Abdel Fattah, F. A. M., Fatma, M., Fawehinmi, O., & Aigbogun, O. (2022). Talent management practices on employee performance: A mediating role of employee engagement in institution of higher learning: Quantitative analysis. Industrial and Commercial Training, 54(4), 589–612. https://doi.org/10.1108/ICT-04-2021-0026

  2. Aheto, S. P. K., Barfi, K. A., Kwesi, C., & Nyagorme, P. (2024). Relationships between online self-regulation skills, satisfaction, and perceived learning among distance education learners. Heliyon, 10(8), e31742. https://doi.org/10.1016/j.heliyon.2024.e31742

  3. Ail, Y. A., & Kumar, P. K. S. (2023). Talent management in the education sector: A comprehensive literature review. Journal of Emerging Technologies and Innovative Research, 10(12), 1–10.

  4. Al Ariss, A., Cascio, W. F., & Paauwe, J. (2014). Talent management: Current theories and future research directions. Journal of World Business, 49(2), 173–179. https://doi.org/10.1016/j.jwb.2013.11.001

  5. Amelia, E., & Rofaida, R. (2023). Talent management in organizations: Systematic literature review. Airlangga Journal of Innovation Management, 4(1), 41–59. https://doi.org/10.20473/ajim.v4i1.51573

  6. Anlesinya, A., & Amponsah-Tawiah, K. (2020). Towards a responsible talent management model. European Journal of Training and Development, 44(2/3), 279–303. https://doi.org/10.1108/EJTD-04-2019-0062

  7. Barkhuizen, E. N., Schutte, N. E., & Smit, A. (2015). Talent management, motivation and service quality of support staff in a public higher education institution. Journal of Public Administration, 50(1), 658–673.

  8. Barkhuizen, E., & Veldtman, G. (2012, July). The impact of talent management practices on the retention of employees in an electricity supply company. Poster presented at the 30th International Congress of Psychology, Cape Town, South Africa.

  9. Barkhuizen, E., Mogwere, P., & Schutte, N. (2014). Talent management, work engagement and service quality orientation of support staff in a higher education institution. Mediterranean Journal of Social Sciences, 5(4), 69–77. https://doi.org/10.5901/mjss.2014.v5n4p69

  10. Barkhuizen, E. (2021). The relationship between talent management, job satisfaction and voluntary turnover intentions of employees in a selected government institution. ResearchGate. https://www.researchgate.net/publication/349866508

  11. Bennett, N., & Martin, C. L. (2025). AI as a talent management tool: An organizational justice perspective. Business Horizons. Advance online publication. https://doi.org/10.1016/j.bushor.2025.03.005

  12. Briki, M., Gherrab, S., & Almi, H. (2024). Leveraging artificial intelligence to optimize talent management in higher education institutions. TRAS, 5(Special Issue on AI and Education, Online Learning and Education), 464–480. https://doi.org/10.3390/tras5040027

  13. Chukwuka, E. J., & Dibie, K. E. (2024). Strategic role of artificial intelligence (AI) on human resource management (HR) employee performance evaluation function. International Journal of Entrepreneurship and Business Innovation, 7(2), 269–282.

  14. Collings, D. G., Mellahi, K., & Cascio, W. F. (2019). Global talent management and performance in multinational enterprises: A multilevel perspective. Journal of Management, 45(2), 540–566. https://doi.org/10.1177/0149206318757018

  15. Dillon, J. D. (2020). The path to AI. TD: Talent Development, 74(1), 24–29.

  16. Do, H., Chu, L. X., & Shipton, H. (2025). How and when AI-driven HRM promotes employee resilience and adaptive performance: A self-determination theory. Journal of Business Research, 192, 115279. https://doi.org/10.1016/j.jbusres.2025.115279

  17. Dogra, S., & Singh, S. (2025). The role of artificial intelligence in human resource management within selected educational institutions. Research Review International Journal of Multidisciplinary, 10(2), 81–92.

  18. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

  19. Ekuma, K. (2024). Artificial intelligence and automation in human resource development: A systematic review. Human Resource Development Review, 23(2), 199–229. https://doi.org/10.1177/15344843241235360

  20. Faqihi, A., & Miah, S. J. (2023). Artificial intelligence-driven talent management system: Exploring the risks and options for constructing a theoretical foundation. Journal of Risk and Financial Management, 16(1), 31. https://doi.org/10.3390/jrfm16010031

  21. Hmoud, B., & Várallyai, L. (2021). Artificial intelligence in talent acquisition: Do we trust it? Journal of Agricultural Informatics, 12(1), 9–19. https://doi.org/10.17700/jai.2021.12.1.644

  22. Huisman, M., Ranschaert, E., Parker, W., Mastrodicasa, D., Koci, M., Pinto de Santos, D., Coppola, F., Morozov, S., Zins, M., Bohyn, C., Koç, U., Wu, J., Veean, S., Fleischmann, D., Leiner, T., & Willemink, M. J. (2021). An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: Expectations, hurdles to implementation, and education. European Radiology, 31(11), 8794–8806. https://doi.org/10.1007/s00330-021-07782-4

  23. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001

  24. Erasmus, B., Naidoo, L., & Joubert, P. (2017). Talent management implementation at an open distance e-learning higher educational institution: The views of senior line managers. International Review of Research in Open and Distributed Learning, 18(3), 83–98. https://doi.org/10.19173/irrodl.v18i3.2950

  25. García, N. L., & Sesé, A. (2024). Measuring the teachers’ talent management. International Journal of Educational Management, 38(2), 568–586. https://doi.org/10.1108/IJEM-04-2023-0152

  26. Jose, S. (2019). Innovation in recruitment and talent acquisition: A study on technologies and strategies adopted for talent management in IT sector. International Journal of Marketing & Human Resource Management, 10(3), 1–8.

  27. Jyoti, J., & Rani, R. (2014). Exploring talent management practices: Antecedents and consequences. International Journal of Management Concepts and Philosophy, 8(4), 220–248. https://doi.org/10.1504/IJMCP.2014.065364

  28. Kamal, M. Y., & Lukman, Z. M. (2017). The effects of talent management on performance management. International Journal of Academic Research in Business and Social Sciences, 7(9), 372–385. https://doi.org/10.6007/IJARBSS/v7-i9/3321

  29. Karaboga, T. (2023). The impact of Industry 4.0 on talent management practices: A systematic review. In Leadership perspectives on effective intergenerational communication and management (pp. 53–71). IGI Global. https://doi.org/10.4018/978-1-6684-8033-3.ch004

  30. Karsenti, T. (2019). Artificial intelligence in education: The urgent need to prepare teachers for tomorrow’s schools. Formation et Profession, 27(1), 112–116. https://doi.org/10.18162/fp.2019.483

  31. Lang, J. (2023). Workforce upskilling: Can universities meet the challenges of lifelong learning? The International Journal of Information and Learning Technology, 40(5), 388–400. https://doi.org/10.1108/IJILT-01-2023-0005

  32. Maity, S. (2019). Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, 38(8), 651–663. https://doi.org/10.1108/JMD-11-2018-0322

  33. Malik, A., De Silva, M. T., Budhwar, P., & Srikanth, N. R. (2021). Elevating talents’ experience through innovative artificial intelligence-mediated knowledge sharing: Evidence from an IT multinational enterprise. Journal of International Management, 27(4), 100871. https://doi.org/10.1016/j.intman.2021.100871

  34. Memon, H., Anwar, S., & Qadir Patoli, A. (2022). Talent management and employee performance: A quantitative assessment during the COVID-19 pandemic. Journal of Development and Social Sciences, 3(4), 608–618. https://doi.org/10.47205/jdss.2022(3-IV)54

  35. Mendy, J., Jain, A., & Thomas, A. (2024). Artificial intelligence in the workplace—Challenges, opportunities and HRM framework: A critical review and research agenda for change. Journal of Managerial Psychology, 40(5), 517–538. https://doi.org/10.1108/JMP-11-2022-0609

  36. Moses Segbenya, M., Senyametor, F., Aheto, S.-P. K., Agormedah, E. K., Nkrumah, K., & Kaedebi-Donkor, R. (2024). Modelling the influence of antecedents of artificial intelligence on academic productivity in higher education: A mixed method approach. Cogent Education, 11(1), 2387943. https://doi.org/10.1080/2331186X.2024.2387943

  37. Rainie, L., Anderson, M., McClain, C., Vogels, E. A., & Gelles-Watnick, R. (2023). AI in hiring and evaluating workers: What Americans think. Pew Research Center. https://www.pewresearch.org/wp-content/uploads/sites/20/2023/04/PI_2023.04.20_AI-in-Hiring_FINAL.pdf

  38. Rani, R., & Nanda, D. (2023). Talent management practices and job satisfaction among faculty in private universities. Journal of Positive School Psychology, 7(2), 544–553.

  39. Rotatori, D., Lee, E. J., & Sleeva, S. (2021). The evolution of the workforce during the fourth industrial revolution. Human Resource Development International, 24(1), 92–103. https://doi.org/10.1080/13678868.2020.1767453

  40. Shao, Y., & Shi, W. (2020). Artificial intelligence (AI) and human resource development. In Proceedings of the 4th International Conference on Education, Management and Social Science (EMSS 2020) (pp. 126–133). Atlantis Press. https://doi.org/10.2991/assehr.k.200318.025

  41. Shah, G. M., Memon, N. A., & Tunio, G. (2021). Need for talent management and investigating its impact on organizational performance of higher education institutes. International Review of Management and Business Research, 10(1), 1–12.

  42. Thakkar, D. (2021). Leveraging AI to transform talent acquisition. International Journal of Artificial Intelligence and Machine Learning, 3(3), 1–8. https://doi.org/10.55529/ijaial.33.1.8

  43. Thunnissen, M. (2016). Talent management: For what, how and how well? An empirical exploration of talent management in practice. Employee Relations, 38(1), 57–72. https://doi.org/10.1108/ER-08-2015-0159

  44. Wamba, S. F., Queiroz, M. M., Jabbour, C. J. C., & Shi, C. V. (2023). Are both generative AI and ChatGPT game changers for 21st-century operations and supply chain excellence? International Journal of Production Economics, 265, 109015. https://doi.org/10.1016/j.ijpe.2023.109015

  45. Wang, J., Xing, Z., & Zhang, R. (2023). AI technology application and employee responsibility. Humanities and Social Sciences Communications, 10(1), 1–17. https://doi.org/10.1057/s41599-023-01608-5

  46. Wijayati, D. T., Rahman, Z., Rahman, M. F., Arifah, I. D., & Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: The moderating role of change leadership. International Journal of Manpower, 43(2), 486–512. https://doi.org/10.1108/IJM-03-2021-0186

  47. Zhang, J. A., Chen, G., O’Kane, C., Xiang, S., & Wang, J. (2022). How employee exploration and exploitation affect task performance: The influence of organizational competitive orientation. The International Journal of Human Resource Management, 33(5), 930–964. https://doi.org/10.1080/09585192.2020.1799231

Perceptions of Youth on Ai Integration in Climate Modeling and Disaster Resilience
Priyanka Prasanth, BBA student, Christ College (Autonomous), Irinjalakuda, Thrissur.
Pages: 30-41 | First Published: 05 Oct 2025
Full text | Abstract | PDF | References | Request permissions

Abstract

In a world that is at the dawn of a triple planetary crisis, where climate disasters that plague ordinary lives have become frequent visitors, we are at a standstill. With lives being lost as every second passes, humanity requires comprehensive solutions. As climate change intensifies the frequency and severity of natural disasters, the integration of Artificial Intelligence into climate modeling and disaster resilience has emerged as a promising approach. 

Through this paper, I aim to explore the perceptions, opinions and concerns of today’s youth, surrounding the role of Artificial Intelligence in enhancing climate modeling and strengthening global disaster response efforts. The study adopts an online survey with snowball sampling in order to collect responses from a wide range of youth. Having collected 100 responses from young people studying in different fields, the study provides an analysis of how the young generation perceives this AI driven shift. The study tells us that today's youth are quick to accept and adopt this changing technology. They are aware of the benefits and risks it brings, and are willing to participate and contribute towards solving the climate crisis. AI development in the field of climate modeling and disaster resilience need not be confined to the experts alone, for now we have an eager and curious young generation, filled with passion and innovative ideas, ready to make a difference.

Keywords: Artificial Intelligence, Climate Modeling, Disaster Resilience, Youth, Climate change, Disaster Management, Youth Perceptions, AI integrations

References

  1. Bordoni, S., Kang, S. M., Shaw, T. A., Simpson, I. R., & Zanna, L. (2025). The futures of climate modeling. Npj Climate and Atmospheric Science, 8(1). https://doi.org/10.1038/s41612-025-00955-8 

  2. Diehr, J., Ogunyiola, A., & Dada, O. (2025). Artificial intelligence and machine learning-powered GIS for proactive disaster resilience in a changing climate. Annals of GIS, 31(2), 287–300. https://doi.org/10.1080/19475683.2025.2473596 

  3. Dehghani, A., Ghomian, Z., Rakhshanderou, S., Khankeh, H., & Kavousi, A. (2022, December 8). Process and components of disaster risk communication in health systems: A thematic analysis. Dehghani | Jàmbá: Journal of Disaster Risk Studies. https://jamba.org.za/index.php/jamba/article/view/1367/2505 

  4. GSDRC. (2016, March 22). What is disaster resilience? - GSDRC. GSDRC - Governance, Social Development, Conflict and Humanitarian Knowledge Services. 

    https://gsdrc.org/topic-guides/disaster-resilience/concepts/what-is-disaster-resilience/ 

  5. Kadow, C., Hall, D. M., & Ulbrich, U. (2020). Artificial intelligence reconstructs missing climate information. Nature Geoscience, 13(6), 408–413. https://doi.org/10.1038/s41561-020-0582-5 

  6. Mugabo Kalisa, G. (2025). The Role of Artificial Intelligence in Climate Modeling. interactions, 1,2. https://smartie.kiu.ac.ug/public/assets/publications/1747656150_9deaa121c51c5babd071.pdf 

  7. Naik, M., Bharani, J. S. S. L., Roy, A., Prakash, V., Yadav, H., & Chetia, B. (2025). AI-driven climate modelling and forecasting: Enhancing environmental resilience through predictive analytics. International Journal of Environmental Sciences, 11, 2025

  8. Nobel Prize Outreach. (2025, August 23). Syukuro Manabe – Facts – 2021. NobelPrize.org.

  9. Singh, V., & Agnihotri, A. (2024). Addressing environmental challenges through artificial intelligence (AI)-powered natural disaster management. Int J Appl Sci Res, 2(5), 485-96. https://journal.multitechpublisher.com/index.php/ijasr/article/view/1413/2008 

  10. Thekdi, S., Tatar, U., Santos, J., & Chatterjee, S. (2022). Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities. Risk Analysis, 43(8), 1641–1656. https://doi.org/10.1111/risa.14038 

  11. What is a climate model? - NCAS. (n.d.). NCAS. https://ncas.ac.uk/learn/what-is-a-climate-model/ 

  12. What is the Anthropocene and why does it matter? | Natural History Museum. (2019, November 26). https://www.nhm.ac.uk/discover/what-is-the-anthropocene.html 

  13. World Climate Research Program. (2025, July 8). CMIP Overview - Coupled Model Intercomparison Project. Coupled Model Intercomparison Project. https://wcrp-cmip.org/cmip-overview/