The Inverge Journal of Social Sciences (IJSS) is an online, open-access journal with double-blind peer review. Academic essays and research articles/papers are all published in the IJSS. Scholars of any age or from any profession are welcome to submit their scholarly research to the IJSS. In its publications, the IJSS covers all social science disciplines, including but not limited to the following fields: Development Studies Economics Entrepreneurship Education Gender Studies International Relations Management Sciences (Marketing, Finance, HRM, Project Management, Information Management, & others) Media Sciences Political Science Psychology Sustainable Development Goals Sociology IJSS is an online, open-access journal that offers publishing and research services to the academic and business sectors. Double-blind peer review is used to manage articles and papers by the IJSS. Frequency: Quarterly Dr. Muhammad Asif Editor-in-Chief Inverge Journal of Social Sciences (IJSS) ISSN (Online): 2959-4359, ISSN (Print): 3007-2018 Facebook: https://www.facebook.com/InvergeJSS/ Note: Authors are urged to use the IJSS online submission platform to submit their work electronically. Please adhere to the guidelines listed for each step of the online submission procedure. Before submitting, kindly adhere to the Author Guidelines as well.
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The Impact of Working Mothers on Children’s Social Behaviours, Classroom Interactions, and Empathy in South Punjab, Pakistan
This research paper has discussed the effects of mother employment on the social behaviours, classroom interactions, and empathies development in South Punjab, Pakistan. Convenience sampling was used to recruit 200 mothers (N=200) all of whom had professional backgrounds (44 percent education, 25 percent medical field and 31 percent other fields). The social-emotional competencies of children were assessed with the help of the maternal reports with the help of the standardized measures that evaluated the social behaviour, interactions with the classmates, and empathy. Findings showed moderate levels of prosocial behaviour (M=2.17, SD=0.21), classroom interactions (M=2.23, SD=0.27) and manifestation of empathy (M=2.10, SD=0.25). The number of job hours and nature of the employment (public versus private sector) were found as important variables and the determinants of children developing socially and emotionally. The results indicate that the South Asian context of maternal employment and child development is a complex issue, and the employment status is not a leading factor that determines social-emotional outcomes of children. Rather, job quality, parental involvement and family dynamics are significant mediating factors. References Abbasi, E. A., Khurshid, D. R., & Zia, D. A. (2025). Effectiveness of play intervention on pre-school students' social emotional development as viewed from teachers' perspective in private sector schools of AJK. ACADEMIA International Journal for Social Sciences, 4(1), 305–317. https://doi.org/10.63056/ACAD.004.01.0073 Amir, T. S., & Kizilbash, S. (2024). Predicaments of married & single working mothers in Pakistan. Academy of Education and Social Sciences Review, 4(4), 505–515. https://doi.org/10.48112/aessr.v4i4.904 Arlianty, L., Setiawan, N., & Achdiani, Y. (2025). Single mother parenting in preschool children's social-emotional development. Indonesian Journal of Community and Special Needs Education, 5(1), 7–16. https://doi.org/10.17509/ijcsne.v5i1.82064 Baranov, V., Bhalotra, S., Biroli, P., & Maselko, J. (2020). Maternal depression, women's empowerment, and parental investment: Evidence from a randomized controlled trial. American Economic Review, 110(3), 824–859. https://doi.org/10.1257/aer.20180511 Del Bono, E., Francesconi, M., Kelly, Y., & Sacker, A. (2016). Early maternal time investment and early child outcomes. The Economic Journal, 126(607), 1879–1912. https://doi.org/10.1111/ecoj.12342 Hay, D., Paine, A. L., Perra, O., Cook, K. V., Hashmi, S., Robinson, C. L., Kairis, V., & Slade, R. (2021). Prosocial and aggressive behavior: A longitudinal study. Monographs of the Society for Research in Child Development, 86(2), 1–152. https://doi.org/10.1111/mono.12427 Hussain, S., & Usman, A. (2025). Social stigmatization, psychological distress and coping strategies among mothers of only female children in rural Punjab, Pakistan. Journal of Adolescent Development & Social Studies, 14(2), 77–96. https://doi.org/10.62345/jads.2025.14.2.77 Izzati, A. N., Hernawati, N., & Islamiah, N. (2024). The influence of mother-child attachment and caregiver-child interaction on preschool children's social-emotional development. Journal of Child, Family and Community Studies, 3(3), 167–177. https://doi.org/10.29244/jcfcs.3.3.167 Johnson, R. C., Kalil, A., & Dunifon, R. E. (2012). Employment patterns of less-skilled workers: Links to children's behavior and academic progress. Demography, 49(2), 305–330. https://doi.org/10.1007/s13524-011-0086-4 NICHD Early Child Care Research Network. (2001). Child care and children's peer interaction at 24 and 36 months: The NICHD Study of Early Child Care. Child Development, 72(5), 1478–1500. https://doi.org/10.1111/1467-8624.00361 Qudsi, H. A., & Rizkillah, R. (2023). The social support, maternal stress, and social-emotional development of preschool children among working mothers family during COVID-19. Journal of Family Sciences, 8(1), 42737–42747. https://doi.org/10.29244/jfs.v8i1.42737 Rashdi, S. M. F., Agha, N., & Hina, B. (2025). The impact of maternal employment on children's educational attainment at primary school level: A sociological study of working mothers in Khairpur City. Inverge Journal of Social Sciences, 4(3), 288–297. https://doi.org/10.63544/ijss.v4i3.167 Sun, J., Liu, J., Liu, M., Feng, Y., & Li, Y. (2023). Maternal warmth and prosocial behaviors among Chinese preschool children: The roles of social competence sibling presence. Early Child Development and Care, 193(2), 2200338–2200351. https://doi.org/10.1080/03004430.2023.2200338 Thabrew, K. C. (2022). Study on how maternal bonding and emotionality of employed women affects work productivity and child development. Sri Lanka Journal of Social Development, 2(1), 14–32. https://doi.org/10.4038/sljsd.v2i1.14 Thomas, S., Kägström, A., Eichas, K., Inam, A., Ferrer-Wreder, L., & Eninger, L. (2023). Children's social emotional competence in Pakistan and Sweden: Factor structure and measurement invariance of the Social Competence Scale (teacher edition). Frontiers in Psychology, 13, Article 1020963. https://doi.org/10.3389/fpsyg.2022.1020963 Waldfogel, J., Han, W.-J., & Brooks-Gunn, J. (2002). The effects of early maternal employment on child cognitive development. Demography, 39(2), 369–392. https://doi.org/10.1353/dem.2002.0021
Copyright (c) 2026 Muhammad Rafiq-uz-Zaman https://creativecommons.org/licenses/by-nc-sa/4.0
Green Intelligence in Finance: Artificial Intelligence-Driven ESG Analytics and Sustainable Investment Performance
This study examined the role of artificial intelligence (AI)-driven Environmental, Social and Governance (ESG) analytics in enhancing sustainable investment performance. While traditional ESG ratings had been widely used in responsible investment strategies, they often suffered from data inconsistency, subjectivity and limited coverage of unstructured sustainability information. AI-based ESG systems were increasingly applied to extract deeper sustainability signals from corporate disclosures, reports and external data sources. Using portfolio-level analysis, this study compared the financial outcomes of portfolios constructed using AI-driven ESG indicators with those based on conventional ESG ratings. The results showed that AI-enhanced high-ESG portfolios achieved higher mean returns and superior Sharpe ratios than both AI-based low-ESG portfolios and traditionally rated ESG portfolios. In addition, AI-driven high-ESG portfolios demonstrated lower downside-risk exposure and smaller maximum drawdowns during market stress, indicating stronger resilience. Regression analysis further revealed that AI-derived ESG scores were more strongly associated with excess returns than traditional ESG metrics. These findings suggested that AI improved the informational efficiency of ESG assessment by capturing more accurate, forward-looking sustainability risks and opportunities. The study concluded that AI-driven ESG analytics strengthened the financial relevance of sustainability integration and supported better-informed investment decision-making. The results carried important implications for investors, regulators and corporations seeking to align AI deployment with high-integrity sustainable finance practices, while also highlighting the need for ethical and transparent AI governance in financial markets. References Albuquerque, R., Koskinen, Y., Yang, S., & Zhang, C. (2020). Resiliency of environmental and social stocks: An analysis of the exogenous COVID-19 market crash. The Review of Corporate Finance Studies, 9(3), 593–621. https://doi.org/10.1093/rcfs/cfaa011 Ali, A., & Rafiq-uz-Zaman, M. (2025). Institutional inertia vs. ethical innovation: A comparative analysis of AI governance at The Islamia University of Bahawalpur and Cambridge University Press. Contemporary Journal of Social Science Review, 3(4), 91–102. https://doi.org/10.63878/cjssr.v3i4.1695 Alshareef, M. N. (2025). Artificial intelligence-enhanced environmental, social, and governance disclosure quality and financial performance nexus in Saudi listed companies under Vision 2030. Sustainability, 17(16), Article 7421. https://doi.org/10.3390/su17167421 Bai, J., & Ng, S. (2023). Machine learning and finance. Journal of Economic Perspectives, 37(2), 195–216. https://doi.org/10.1257/jep.37.2.195 Berg, F., Kölbel, J., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26(6), 1315–1344. https://doi.org/10.1093/rof/rfac033 Broadstock, D. C., Chan, K., Cheng, L. T. W., & Wang, X. (2021). The role of ESG performance during times of financial crisis: Evidence from COVID-19 in China. Finance Research Letters, 38, Article 101716. https://doi.org/10.1016/j.frl.2020.101716 Cao, J., Li, J., Li, W., & Zhou, Y. (2023). ESG, big data and investment decision-making. Journal of International Financial Markets, Institutions and Money, 85, Article 101757. https://doi.org/10.1016/j.intfin.2023.101757 Carlei, V. (2025). Outperforming ESG stocks portfolios: A machine learning approach to generate alpha on the S&P 500. Expert Systems with Applications, 260, Article 120157. https://doi.org/10.1016/j.eswa.2025.120157 Chen, M. A., Wu, Q., & Yang, B. (2019). How valuable is FinTech innovation? Review of Financial Studies, 32(5), 2062–2106. https://doi.org/10.1093/rfs/hhy130 Christensen, D. M., Hail, L., & Leuz, C. (2021). Mandatory CSR and sustainability reporting: Economic analysis and literature review. Review of Accounting Studies, 26(3), 1176–1248. https://doi.org/10.1007/s11142-021-09609-5 Dorfleitner, G., Halbritter, G., & Nguyen, M. (2022). The risk-return-ESG profile of sustainable mutual funds. Journal of Asset Management, 23(3), 177–192. https://doi.org/10.1057/s41260-021-00229-3 Drempetic, S., Klein, C., & Zwergel, B. (2020). The influence of firm size on the ESG score: Corporate sustainability ratings under review. Journal of Business Ethics, 167(2), 333–360. https://doi.org/10.1007/s10551-019-04164-1 Fatemi, A., Glaum, M., & Kaiser, S. (2018). ESG performance and firm value: The moderating role of disclosure. Global Finance Journal, 38, 45–64. https://doi.org/10.1016/j.gfj.2017.03.001 Fernando, G. D., Tripathy, A., & Truong, C. (2022). Corporate sustainability and firm performance: Evidence from global firms. Journal of Business Research, 153, 38–52. https://doi.org/10.1016/j.jbusres.2022.08.011 Friede, G., Busch, T., & Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2,000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210–233. https://doi.org/10.1080/20430795.2015.1118917 Gibson Brandon, R., Krueger, P., & Schmidt, P. S. (2021). ESG rating disagreement and stock returns. Financial Analysts Journal, 77(4), 104–127. https://doi.org/10.1080/0015198X.2021.1950807 Grewal, J., Hauptmann, C., & Serafeim, G. (2023). Material sustainability information and stock price informativeness. Journal of Accounting and Economics, 75(1), Article 101430. https://doi.org/10.1287/mnsc.2021.4302 Hamdouni, A. (2025). The role of artificial intelligence in enhancing ESG performance. Journal of Risk and Financial Management, 18(10), Article 572. https://doi.org/10.3390/jrfm18100572 Huang, D., Huang, J., & Wan, J. (2023). ESG investment: A review of the literature. Pacific-Basin Finance Journal, 76, Article 102047. https://doi.org/10.1016/j.pacfin.2022.102047 Huang, R., & Renneboog, L. (2020). The effects of ESG ratings on firm performance. European Corporate Governance Institute - Finance Working Paper No. 722/2020. https://doi.org/10.2139/ssrn.3722732 Krueger, P., Sautner, Z., & Starks, L. T. (2020). The importance of climate risks for institutional investors. Review of Financial Studies, 33(3), 1067–1111. https://doi.org/10.1093/rfs/hhz137 Kumar, S., Managi, S., & Matsuda, A. (2022). Stock market response to COVID-19: Evidence from sustainability indices. Economic Analysis and Policy, 74, 44–58. https://doi.org/10.1016/j.eap.2022.01.004 Li, F., Mai, F., Shen, R., & Yan, X. (2020). Measuring corporate culture using machine learning. Review of Financial Studies, 33(5), 2326–2376. https://doi.org/10.1093/rfs/hhz080 Li, Z., Liao, G., & Wang, Z. (2023). ESG performance and financial performance: Evidence from global firms. Technological Forecasting and Social Change, 189, Article 122337. https://doi.org/10.1016/j.techfore.2023.122337 Lim, T. (2024). Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways. Artificial Intelligence Review, 57(4), Article 76. https://doi.org/10.1007/s10462-024-10708-3 Liu, Y., Song, J., Zhou, B., & Liu, J. (2025). Artificial intelligence applications and corporate ESG performance. International Review of Economics & Finance, 104, Article 104559. https://doi.org/10.1016/j.iref.2025.104559 Pedersen, L. H., Fitzgibbons, S., & Pomorski, L. (2021). Responsible investing: The ESG-efficient frontier. Journal of Financial Economics, 142(2), 572–597. https://doi.org/10.1016/j.jfineco.2020.11.001 Rafiq-uz-Zaman, M. (2022). Redesign for 21st-century skills: Preparing learners for a rapidly changing workforce. Journal of Business Insight and Innovation, 1(2), 89–102. https://doi.org/10.5281/zenodo.17844804 Rafiq-uz-Zaman, M. (2023). Teacher training needs for skill-based education: A review of competencies, barriers, and professional development gaps. Inverge Journal of Social Sciences, 2(3), 166–182. https://doi.org/10.63544/ijss.v2i3.212 Raghunandan, A., & Rajgopal, S. (2022). Do ESG funds make stakeholder-friendly investments? Review of Accounting Studies, 27(3), 822–863. https://doi.org/10.1007/s11142-021-09636-y Walz, U., & Wranik, P. (2023). Artificial intelligence in finance: Applications, risks and regulation. Journal of Banking Regulation, 24(1), 125–143. https://doi.org/10.1057/s41261-022-00194-5 Whelan, T., Atz, U., Van Holt, T., & Clark, C. (2021). ESG and financial performance: Uncovering the relationship. Journal of Sustainable Finance & Investment, 11(4), 311–330. https://doi.org/10.1080/20430795.2021.1920510 Widyawati, L. (2020). A systematic literature review of socially responsible investment and environmental social governance metrics. Business Strategy and the Environment, 29(2), 619–637. https://doi.org/10.1002/bse.2393 Wong, W., & Zhang, X. (2022). Is ESG investing a fad or the future? Evidence from global stock markets. Journal of International Financial Markets, Institutions and Money, 80, Article 101618. https://doi.org/10.1016/j.intfin.2022.101618 Xiao, Y., & Xiao, L. (2025). The impact of artificial intelligence-driven ESG practices on sustainable development performance of Chinese central state-owned enterprises. Scientific Reports, 15, Article 8548. https://doi.org/10.1038/s41598-025-17046-6
Copyright (c) 2026 Hind Gatoi, Islam Belhaoua, Kashf Akhtar , Miqdad Qadir, Nida Mohammad , Muhammad Ali https://creativecommons.org/licenses/by-nc-sa/4.0
The Impact of Educational Leadership Styles on Teacher Productivity: A Comprehensive Review
Teacher productivity, teacher motivation, and effectiveness of the institutions are core elements that depend on educational leadership. This is a rigorous comprehensive literature review that summarizes 64 empirical studies that were published in 2016-2025 to explore the effects of different leadership styles on teacher productivity in learning institutions. Based on the PRISMA framework and quality assessment programs, we reviewed studies in K-12 and higher education settings. The results indicate that transformational leadership which involves visionary inspiration and mentorship as well as individualized consideration are associated to have the greatest effect on teacher productivity (b = 0.52-0.70), instructional leadership which is based on pedagogical support (b = 0.45-0.65) and democratic leadership based on collaboration (b = 0.40-0.55). Transactional leadership has a small short-term impact (b = 0.20-0.35) but is not sustainable whereas autocratic and laissez-faire leadership styles have negative implications on teacher performance. The most important mediating variables are job satisfaction, school climate, and professional development engagement. It is important to note that the effectiveness of leadership is heavily contextual such as the resources available in the school, the structure of governance, the cultural values and the socioeconomic context. Instead of adopting the single style, integrated, hybrid methods involving transformational vision, instructional expertise, and collaborative structures are more effective. The review marks out such crucial problems as the administrative overload, resource limitations, and the lack of preparedness among leaders. The main implications of the study to practitioners will be the creation of multifaceted leadership competence, instructional focus, positive school climate, and lifelong professional growth. The policymakers would need to enhance principal preparation, provide an environment where effective leadership can be achieved and align accountability and support development. Future studies ought to examine contextual moderators, implementation processes and long-term sustainability of leadership strategies. This review establishes that strategic educational leadership is one of the pillars of investment in enhancing teacher productivity and organizational performance. References Abbasi, S., Abbasi, P., Alvi, H., Murk, T., & Sajjad, R. (2025). The impact of leadership styles on teacher effectiveness: A study in Larkana schools. The Critical Review of Social Sciences Studies. https://doi.org/10.59075/08p39x93 Adriantoni, A., Komariah, A., Nurdin, D., & Herawan, E. (2023). The effect of transformational leadership and madrasah climate on elementary school teacher performance. Journal of Innovation in Educational and Cultural Research, 4(2). https://doi.org/10.46843/jiecr.v4i2.647 Ahmed, A. A. (2024). The impact of principal leadership styles on teachers’ job performance in primary schools. Human Nature Journal of Social Sciences. https://doi.org/10.71016/hnjss/rzk5dd68 Akullo, E. A., & Kamanyire, V. (2023). Head teacher leadership styles and teacher performance in primary schools in Kaabong District, Uganda. Journal of Education and Practice. https://doi.org/10.47941/jep.1182 Alzoraiki, M., Ahmad, A. R., Ateeq, A., & Milhem, M. (2024). The role of transformational leadership in enhancing school culture and teaching performance in Yemeni public schools. Frontiers in Education. https://doi.org/10.3389/feduc.2024.1413607 Andriadi, D., & Sulistiyo, U. (2024). The influence of transformational and instructional leadership styles of school principals on teachers’ performance, motivation, job satisfaction, and student achievement in primary and secondary schools. PPSDP International Journal of Education, 3(2). https://doi.org/10.59175/pijed.v3i2.335 Angwaomaodoko, & Anthony, E. (2023). The effect of leadership styles on teacher job satisfaction in Nigerian secondary schools. International Research in Education, 11(2). https://doi.org/10.5296/ire.v11i2.21012 Arina, Y., Revita, Y., Gistituati, N., & Rusdinal, R. (2023). The influence of principals’ participative leadership style and work climate on public middle school teacher performance. Edunesia: Jurnal Ilmiah Pendidikan, 4(3). https://doi.org/10.51276/edu.v4i3.487 As-Sadeq, H. A., & Khoury, G. (2006). Leadership styles in the Palestinian large-scale industrial enterprises. Journal of European Industrial Training. https://doi.org/10.1108/02621710610692043 Bhatti, N., Maitlo, G. 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Leading with impact: The relationship between school heads’ multifactor leadership styles and teachers’ job satisfaction in public secondary schools. British Journal of Teacher Education and Pedagogy, 4(6). https://doi.org/10.32996/bjtep.2025.4.6.1 Ed-Valsania, S., Lagua, A., & Moriano, J. A. (2022). Burnout: A review of theory and measurement. International Journal of Environmental Research and Public Health, 19(3), Article 1780. https://doi.org/10.3390/ijerph19031780 Guin, K. (2004). Chronic teacher turnover in urban elementary schools. Education Policy Analysis Archives, 12(42). https://doi.org/10.14507/epaa.v12n42.2004 Hadijah, N. (2024). The impact of leadership styles on teacher performance. Research Invention Journal of Law, Communication and Languages. https://doi.org/10.59298/rijlcl/2024/411318 Han, Y. (2025). The impact of leadership styles on effective education management. Forum for Linguistic Studies, 7(2). https://doi.org/10.30564/fls.v7i2.7839 Harianto, J. E., Umam, F. C., Ginanjar, F., Hidayat, Y., & Muiz, A. (2025). The impact of leadership styles on school performance: A comparative study of educational management models. International Journal for Science Review, 2(3). https://doi.org/10.71364/ijfsr.v2i3.23 Harsoyo, R. (2022). Bernard M. Bass’s transformational leadership theory and its application in improving the quality of Islamic educational institutions. Sajie: South Asian Journal of Islamic Education, 3(2). https://doi.org/10.21154/sajiem.v3i2.112 Hossen, B. (2024). Administrators’ leadership styles in Industrial Revolution 4.0 toward a proposed leadership educational model. International Multidisciplinary Research Journal. https://doi.org/10.54476/ioer-imrj/196274 Htun, S. S., Chakkaravarthy, M., & Bhaumik, A. (2025). Impact of emotional intelligence and leadership styles on employees’ job performance: A review paper. Journal of Multidisciplinary Research for SMET, 1(1). https://doi.org/10.64297/jmrsmet.v1i1.3 Hutasuhut, N. A., Simbolon, A. M. Y., Salsabilah, D., Siregar, R., & Siregar, S. A. (2025). The role of educational leadership in improving educational quality. Academia: Jurnal Inovasi Riset Akademik, 5(4). https://doi.org/10.51878/academia.v5i4.8226 Jaarsveld, L. V., Mentz, P. J., & Ellis, S. (2019). Implementing the multifactor leadership questionnaire (MLQ) in a challenging context. International Journal of Educational Management. https://doi.org/10.1108/IJEM-02-2018-0041 Joseph, C., & Sultana, M. (2025). Leadership styles of school heads and their impact on teacher performance: A qualitative study in private schools of District South Karachi. The Critical Review of Social Sciences Studies. https://doi.org/10.59075/qnfe3s74 Kairupan, D. (2023). Transactional and transformational leadership styles on the effectiveness of work from home and work productivity. Telaah Bisnis, 24(1). https://doi.org/10.35917/tb.v24i1.335 Khan, F. (2023). 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East African Scholars Journal of Economics, Business and Management, 8(1). https://doi.org/10.36349/easjebm.2025.v08i01.006 Noor, S., Adnan, M., Rehman, L., & Shafiq, A. (2024). The impact of leadership styles on teacher self-efficacy and professional development. Bulletin of Business and Economics. https://doi.org/10.61506/01.00424 Novita, P., Cahya, Y. F., & Meilani, P. (2025). Reconstructing educational leadership theory: Building a holistic framework for 21st century schools. Feedforward Journal of Human Resource, 5(1). https://doi.org/10.19166/ff.v5i1.9577 Pacia, D. R., & Guevarra, P. M. (2023). Influence of path–goal theory of leadership styles and the moderating role of task structure on teachers’ satisfaction, motivation, and performance. International Journal of Multidisciplinary Applied Business and Education Research, 4(7). https://doi.org/10.11594/ijmaber.04.07.15 Pallangyo, H., & Mwila, P. (2025). Leadership styles and teacher job performance: Enhancing productivity in Tanzanian secondary schools. Global Journal of Basic Education, 1(1). https://doi.org/10.61227/gjbe.v1i1.169 Pitriani, E. (2024). Educational leadership in improving school performance and teacher professional development: A literature review. PPSDP International Journal of Education, 3(2). https://doi.org/10.59175/pijed.v3i2.312 Puspitadani, E., Yudea, F., & Loo, F. (2022). Educational leadership and learning quality: The influence of the principal’s leadership style on teacher performance. Jurnal Ilmu Pendidikan dan Humaniora, 11(3). https://doi.org/10.35335/jiph.v11i3.24 Rafiq-uz-Zaman, M. (2023). Analysis of leadership styles and decision-making effectiveness in public sector universities in Pakistan: A systematic review. Inverge Journal of Social Sciences, 2(1), 72–91. https://doi.org/10.63544/ijss.v2i1.209 Rafiq-uz-Zaman, M. (2025). Unpacking the leadership and governance crisis in Pakistani universities: Structural, political, and institutional drivers. The Critical Review of Social Sciences Studies, 3(4), 901–912. https://doi.org/10.59075/pk8zan95 Rafiq-uz-Zaman, M., Bano, S., & Naveed, Y. (2025). Comparative analysis of authoritative and democratic leadership styles and their impact on school management effectiveness. Inverge Journal of Social Sciences, 4(2), 105–117. https://doi.org/10.63544/ijss.v4i2.132 Romy, E. (2024). Examining the impact of leadership approach and principal’s drive on educator productivity. Jurnal Ekonomi, Bisnis & Entrepreneurship. https://doi.org/10.55208/bzq06z82 Saepurohman, L., & Satori, D. (2021). The effect of servant leadership on teacher performance. Advances in Social Science, Education and Humanities Research. https://doi.org/10.2991/assehr.k.210212.040 Sarah, M., Bariham, I., & Quansah, J. Y. D. (2023). Impact of headteachers’ leadership styles on teachers’ job performance in Sagnarigu Municipality, Ghana. Social Education Research. https://doi.org/10.37256/ser.4220232903 Sari, N., Ilham, D., Hasbi, H., & Mardhiah, M. (2025). The role of leadership styles in improving educational standards. International Journal of Asian Education, 6(1). https://doi.org/10.46966/ijae.v6i1.467 Sarwar, U., Tariq, R., & Yong, Q. Z. (2022). Principals’ leadership styles and their impact on teachers’ performance at college level. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.919693 Sitiol, D. L., Mwamba, G., & Kobuthi, E. N. (2025). Head teachers’ leadership styles and pupils’ KCPE performance: A case study of Imaroro Ward, Kenya. Human Resource and Leadership, 5(1), 62–79. https://doi.org/10.70619/vol5iss1pp62-79 Sugiyanto, H. (2025). Leadership styles, quality of education, and educational institution management: A systematic review from a sustainable education perspective. 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Copyright (c) 2026 Atif Nadeem, Saima Iram , Haseen Akhtar https://creativecommons.org/licenses/by-nc-sa/4.0
Enterprise Intelligence 5.0: Human AI Co-Creation Models for Strategic Leadership, Innovation, and Competitive Advantage
This study examined Enterprise Intelligence 5.0 as a human–AI co-creation paradigm where artificial intelligence functions as a strategic partner, not a substitute. A quantitative design investigated how human–AI co-creation, leadership orientation, organizational learning, and trust in AI influence innovation performance and competitive advantage. Data from a structured survey of managers in AI-enabled organizations revealed that human–AI co-creation exerted the strongest positive effect on innovation, followed by leadership orientation, organizational learning, and trust. Innovation increased significantly with higher AI adoption, showing Enterprise Intelligence 5.0 enhances exploratory capability, creativity, and strategic agility. The findings indicate AI’s value is realized not through technology alone, but via quality human–AI collaboration supported by ethical leadership, a learning culture, and governance. Theoretically, the study frames Enterprise Intelligence 5.0 as a socio-technical system of augmentation, not automation. Practically, it emphasizes leadership commitment, transparency, AI literacy, and responsible governance to sustain innovation. Future research should adopt longitudinal and mixed methods to explore evolving co-creation dynamics. A key insight is the importance of iterative feedback loops allowing humans to refine AI, boosting accuracy and trust. Organizations with co-learning environments and psychological safety reported higher adoption and innovation. Integrating AI into cross-functional workflows accelerated decision-making and data-driven experimentation. Successful deployment relies on ethical oversight and inclusivity, aligning AI with organizational values. Early-adopting sectors like healthcare and finance saw gains in personalization and risk management. Thus, Enterprise Intelligence 5.0 is more about strategic human-machine alignment than technological sophistication. Sustaining advantage requires continuous skill development, interdisciplinary collaboration, and governance frameworks balancing innovation with accountability. Future studies should explore sector-specific barriers and AI's long-term impact on workforce dynamics and organizational resilience. References Akpan, I. J., Soopramanien, D., & Kwak, D. H. (2022). Cutting-edge technologies for small business and innovation in the era of COVID-19 global health pandemic. Journal of Small Business & Entrepreneurship, 34(2), 123–140. https://doi.org/10.1080/08276331.2020.1799294 Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making. Journal of Information Technology, 35(1), 37–57. https://doi.org/10.1177/0268396219862972 Aroles, J., Mitev, N., & Vaujany, F.-X. de. (2019). 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Artificial intelligence and innovation management: A review and research agenda. Journal of Innovation & Knowledge, 6(3), 253–269. https://doi.org/10.1016/j.jik.2020.11.002 Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00737-0 Ivanov, S. (2020). The impact of automation on tourism and hospitality jobs. Tourism Management, 81, 104311. https://doi.org/10.1016/j.tourman.2020.104311 Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision-making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007 Le, P. B., & Lei, H. (2019). The mediating role of trust in knowledge sharing and organizational learning. Leadership & Organization Development Journal, 40(1), 68–84. https://doi.org/10.1108/LODJ-06-2018-0215 Leyer, M., & Schneider, S. (2023). 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Copyright (c) 2026 Sohrab Khan Magsi, Muhammad Ahmad, Muhammad Amoon Khalid, Muhammad Irfan Syed, Jafar Ali, Maham Fazal https://creativecommons.org/licenses/by-nc-sa/4.0
Awareness, Competence, and Perceptions of Augmented Reality, Virtual Reality, and the Metaverse among Pakistani University Librarians
This study assesses the readiness of Pakistani university librarians for Augmented Reality (AR), Virtual Reality (VR), and Metaverse technologies by examining their awareness, competence, interest, and perceptions. A quantitative survey design was employed, with data collected from 96 librarians via a validated online questionnaire. Descriptive statistics were used to analyse demographic profiles, awareness, proficiency, interest levels, and perceived future roles of these technologies. Librarians demonstrated moderate to high awareness of AR and the Metaverse but lower awareness of VR. Practical proficiency was limited across all three technologies. However, strong interest was expressed in learning and integrating these tools into library services. Perceptions of the Metaverse’s future role varied, with many anticipating moderates to transformative impacts. The study calls for targeted training programs, curriculum revisions, and institutional investments in infrastructure and partnerships to bridge the competence gap and foster technology adoption. This is the first empirical study in Pakistan to focus on librarians’ readiness for immersive technologies, offering foundational insights for policy, practice, and future research in developing countries. References Abid, S., Awan, B., Ismail, T., & et al. (2019). Artificial intelligence: Medical students' attitude in district Peshawar Pakistan. Pakistan Journal of Public Health, 9(1), 19–21. Adeyemi, I. O., Sulaiman, K. A., Abdulsalam, Z. M., & et al. (2023). Virtual and augmented reality as predictors of users’ intention to use Lagos State Public Library, Lagos State, Nigeria. The Electronic Library, 41(5), 682–699. https://doi.org/10.1108/EL-11-2022-0257 Ajani, Y. A., Enakrire, R. T., Oladokun, B. D., & et al. (2023). Reincarnation of libraries via metaverse: A pathway for a sustainable knowledge system in the digital age. Business Information Review. Advance online publication. https://doi.org/10.1177/02663821231208044 Alam, A. (2022). Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: Reforming education in the age of artificial intelligence. In R. N. Shaw, S. Das, V. Piuri, & et al. (Eds.), Advanced computing and intelligent technologies (pp. 395–406). Springer. https://doi.org/10.1007/978-981-16-2164-2_30 Alex, K., Vrushank, S., & Sita, R. (2023). Handbook of research on AI-based technologies and applications in the era of the metaverse. IGI Global. https://doi.org/10.4018/978-1-7998-9805-1 Alkhwaldi, A. F. (2023). Understanding learners’ intention toward metaverse in higher education institutions from a developing country perspective: UTAUT and ISS integrated model. Kybernetes. Advance online publication. https://doi.org/10.1108/K-02-2023-0286 Anna, N. E. V., Harisanty, D., & Ismail, N. (2023). Libraries on metaverse, do they exist? Library Hi Tech News, 40(6), 1–2. https://doi.org/10.1108/LHTN-09-2023-0105 Asif, M., Shahid, S., & Rafiq-uz-Zaman, M. (2025). Immersive technologies, awe, and the evolution of retail in the metaverse. International Premier Journal of Languages & Literature, 3(4), 713–748. https://ipjll.com/ipjll/index.php/journal/article/view/295 Asif, M., Ali, A., & Shaheen, F. A. (2025). Assessing the Effects of Artificial Intelligence in Revolutionizing Human Resource Management: A Systematic Review. Social Science Review Archives, 3(4), 2887–2908. https://doi.org/10.70670/sra.v3i3.1055 Aslam, M., & Asif, M. (2025). Organizational Power Structures and the Reproduction of Gender Inequality. Apex Journal of Social Sciences, 4(1), 57-67. https://apexjss.com/index.php/AJSS/article/view/19 Dahya, N., King, W. E., Lee, K. J., & et al. (2021). Perceptions and experiences of virtual reality in public libraries. Journal of Documentation, 77(3), 617–637. https://doi.org/10.1108/JD-07-2020-0117 Dalili Saleh, M., Salami, M., Soheili, F., & et al. (2021). Augmented reality technology in the libraries of universities of medical sciences: Identifying the application, advantages and challenges and presenting a model. Library Hi Tech, 40(6), 1782–1795. https://doi.org/10.1108/LHT-01-2021-0006 Daradkeh, M. (2023a). Intelligent librarians in the metaverse: Concept, competencies, and career pathways. In Handbook of research on advancements of contactless technology and service innovation in library and information science (pp. 45–63). IGI Global. https://doi.org/10.4018/978-1-7998-7957-9.ch004 Daradkeh, M. (2023b). Intelligent libraries: Using metaverse as an enabling technology. In Applications of neuromarketing in the metaverse (pp. 1–18). IGI Global. https://doi.org/10.4018/978-1-6684-7126-7.ch001 Daradkeh, M. (2023c). Metaverse librarians: A new profession for intelligent libraries. In Handbook of research on consumer behavioral analytics in metaverse and the adoption of a virtual world (pp. 253–272). IGI Global. https://doi.org/10.4018/978-1-6684-7936-0.ch016 Denda, K., & Hunter, J. (2016). Building 21st century skills and creating communities: A team-based engagement framework for student employment in academic libraries. Journal of Library Administration, 56(3), 251–265. https://doi.org/10.1080/01930826.2016.1151359 Devagiri, J. S., Paheding, S., Niyaz, Q., & et al. (2022). Augmented reality and artificial intelligence in industry: Trends, tools, and future challenges. Expert Systems with Applications, 207, Article 118002. https://doi.org/10.1016/j.eswa.2022.118002 Diseiye, O., Ukubeyinje, S. E., Oladokun, B. D., & et al. (2024). Emerging technologies: Leveraging digital literacy for self-sufficiency among library professionals. Metaverse Basic and Applied Research, 3, 59–59. 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The information needs of users in the metaverse communities: A grounded theory study. Library Hi Tech. Advance online publication. https://doi.org/10.1108/LHT-10-2023-0273 Guo, Y., Yuan, Y., Li, S., & et al. (2023). Applications of metaverse-related technologies in the services of US urban libraries. Library Hi Tech. Advance online publication. https://doi.org/10.1108/LHT-05-2023-0175 Holappa, H., Ylipulli, J., Rautiainen, S., & et al. (2018). VR application for technology education in a public library. In Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia (pp. 521–527). Association for Computing Machinery. https://doi.org/10.1145/3282894.3289719 Hollister, J. M., & Lee, J. (2022). An exploratory study on virtual reality and related technologies in terminal LIS degree programs in the United States and South Korea. 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Beyond mouse and keyboard: Expanding design considerations for information visualization interactions. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2689–2698. https://doi.org/10.1109/TVCG.2012.172 Lee, J., Hollister, J. M., Lim, T., & et al. (2022). A case review for the design of VR-based training for enhancing empathy and cultural competency of public librarians. International Journal of Knowledge Content Development & Technology, 12(Special Issue), 81–100. https://doi.org/10.34163/ijkcdt.2022.12.s.81 Leetaru, K. H. (2015). Mining libraries: Lessons learned from 20 years of massive computing on the world’s information. Information Services & Use, 35(1–2), 31–50. https://doi.org/10.3233/ISU-150749 Li, X., & Zhao, Y. (2023). Exploring the visual interaction design for Eurasia University Library’s digital twin model under the metaverse study. Journal of Electronics and Information Science, 8(4), 1–6. https://doi.org/10.22158/jeis.v8n4p1 Librarian’s perception and skill sets for the use of metaverse in universities in Nigerian. (2024). NIU Journal of Social Sciences. Advance online publication. Liu, T.-Y., Tan, T.-H., & Chu, Y.-L. (2010). QR code and augmented reality-supported mobile English learning system. In X. Jiang, M. Y. Ma, & C. W. Chen (Eds.), Mobile multimedia processing: Fundamentals, methods, and applications (pp. 37–52). Springer. https://doi.org/10.1007/978-3-642-12349-8_3 Lo, L. S. (2023). AI policies across the globe: Implications and recommendations for libraries. IFLA Journal, 49(4), 645–649. https://doi.org/10.1177/03400352231193400 Lund, B. D., Khan, D., & Yuvaraj, M. (2024). ChatGPT in medical libraries, possibilities and future directions: An integrative review. Health Information & Libraries Journal, 41(1), 4–15. https://doi.org/10.1111/hir.12506 Margam, M. (2024). Beyond reality: Metaverse technologies revolutionizing libraries and elevating user engagement. Library Hi Tech News. Advance online publication. https://doi.org/10.1108/LHTN-09-2024-0138 Mayesti, N., Huang, C. H., Azmir, A. F., & et al. (2024). Librarians’ views of the readiness of university libraries in Indonesia to adopt virtual and augmented reality. Digital Library Perspectives. Advance online publication. https://doi.org/10.1108/DLP-07-2024-0073 Mupaikwa, E. (2025). The application of artificial intelligence and machine learning in academic libraries. In Encyclopedia of information science and technology, sixth edition (pp. 1–18). IGI Global. https://doi.org/10.4018/978-1-6685-2666-6.ch001 Noh, Y. (2023a). A study on the developmental direction of the metaverse libraries for the future. Libri, 73(3), 239–252. https://doi.org/10.1515/libri-2022-0078 Noh, Y. (2023b). A study on the discussion on Library 5.0 and the generation of Library 1.0 to Library 5.0. Journal of Librarianship and Information Science, 55(4), 889–905. https://doi.org/10.1177/09610006221123704 Oladokun, B. D., Enakrire, R. T., & Ajani, Y. A. (2023). Metaliteracy advocacy: The need for libraries to engage users in the metaverse. Business Information Review. Advance online publication. https://doi.org/10.1177/02663821231209602 Oladokun, B. D., Yahaya, D. O., & Enakrire, R. T. (2023). Moving into the metaverse: Libraries in virtual worlds. Library Hi Tech News. Advance online publication. https://doi.org/10.1108/LHTN-08-2023-0091 Pham, S. T. H., & Sampson, P. M. (2022). The development of artificial intelligence in education: A review in context. Journal of Computer Assisted Learning, 38(5), 1408–1421. https://doi.org/10.1111/jcal.12709 Riedl, M. O. (2019). Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, 1(1), 33–36. https://doi.org/10.1002/hbe2.137 Saibakumo, W. T. (2021). Awareness and acceptance of emerging technologies for extended information service delivery in academic libraries in Nigeria. Library Philosophy and Practice. https://digitalcommons.unl.edu/libphilprac/5432 Sediyaningsih, S., Ristiyono, M. P., Launggu, K., & et al. (2023). De-contextual communication: Factors influencing usage intentions of metaverse technology in digital library services. Heliyon, 9(10), Article e20751. https://doi.org/10.1016/j.heliyon.2023.e20751 Smilansky, S. (2017). Experiential marketing: A practical guide to interactive brand experiences (2nd ed.). Kogan Page. Solak, E., & Cakir, R. (2015). Exploring the effect of materials designed with augmented reality on language learners’ vocabulary learning. Journal of Educators Online, 12(2), 50–72. https://doi.org/10.9743/JEO.2015.3/106 Subaveerapandiyan, A., & Gozali, A. A. (2024). AI in Indian libraries: Prospects and perceptions from library professionals. Open Information Science, 8(1), Article 20220147. https://doi.org/10.1515/opis-2022-0147 Subaveerapandiyan, A., Sunanthini, C., & Amees, M. (2023). A study on the knowledge and perception of artificial intelligence. IFLA Journal, 49(3), 503–513. https://doi.org/10.1177/03400352231188299 Sumadio, D. D., & Rambli, D. R. A. (2010). Preliminary evaluation on user acceptance of the augmented reality use for education. In 2010 Second International Conference on Computer Engineering and Applications (pp. 461–465). IEEE. https://doi.org/10.1109/ICCEA.2010.5445691 Sureephong, P., Chernbumroong, S., Niemsup, S., & et al. (2024). Exploring the impact of the gamified metaverse on knowledge acquisition and library anxiety in academic libraries. Information Technology and Libraries, 43(1). https://doi.org/10.5860/ital.v43i1.16224 Tammaro, A. M. (2024). Editorial: Digital libraries as sociotechnical systems. Digital Library Perspectives, 40(1), 1–3. https://doi.org/10.1108/DLP-01-2024-0005 Tang, Y. (2021). Help first-year college students to learn their library through an augmented reality game. The Journal of Academic Librarianship, 47(1), Article 102294. https://doi.org/10.1016/j.acalib.2020.102294 Tella, A., Ajani, Y. A., & Ailaku, U. V. (2023). Libraries in the metaverse: The need for metaliteracy for digital librarians and digital age library users. Library Hi Tech News. Advance online publication. https://doi.org/10.1108/LHTN-10-2023-0127 Tsou, M.-H., & Mejia, C. (2023). Beyond mapping: Extend the role of cartographers to user interface designers in the metaverse using virtual reality, augmented reality, and mixed reality. Cartography and Geographic Information Science. Advance online publication. https://doi.org/10.1080/15230406.2023.2279431 van Arnhem, J.-P., & Spiller, J. M. (2014). Augmented reality for discovery and instruction. Journal of Web Librarianship, 8(2), 214–230. https://doi.org/10.1080/19322909.2014.897107 Wang, C.-Y., & Lin, J. J. H. (2023). Utilizing artificial intelligence to support analyzing self-regulated learning: A preliminary mixed-methods evaluation from a human-centered perspective. Computers in Human Behavior, 144, Article 107721. https://doi.org/10.1016/j.chb.2023.107721 Yi Xiao, D. (2000). Experiencing the library in a panorama virtual reality environment. Library Hi Tech, 18(2), 177–184. https://doi.org/10.1108/07378830010341035 Yoon, J., Andrews, J. E., & Ward, H. L. (2021). Perceptions on adopting artificial intelligence and related technologies in libraries: Public and academic librarians in North America. Library Hi Tech, 40(6), 1893–1915. https://doi.org/10.1108/LHT-04-2021-0131
Copyright (c) 2026 Hina Sardar, Muhammad Kabir Khan https://creativecommons.org/licenses/by-nc-sa/4.0
Media, Mind, and Crime: Psychological and Communicative Dimensions of Criminal Representation
This study examined how media representations of crime influenced public psychology and communicative meaning-making within contemporary digital and news environments. Focusing on the interaction between media framing, emotional processing, and audience interpretation, the research investigated how crime narratives shaped fear of crime, trust in criminal justice institutions, and perceived social risk. A mixed-methods design was employed, combining a systematic content analysis of crime-related media with a survey of media users to assess psychological and attitudinal outcomes. The results showed that higher exposure to crime-focused media was associated with increased fear, heightened perceptions of crime severity, and declining confidence in law enforcement and judicial systems. Sensationalized framing significantly intensified emotional arousal and threat perception, whereas contextualized reporting produced more balanced and less fear-driven evaluations. These findings indicated that media narratives did not simply reflect crime but actively constructed symbolic meanings about danger, morality, and social order. The study also found that digital platforms and true-crime formats amplified these effects by encouraging emotional engagement, repetition, and participatory interpretation of criminal events. By integrating psychological theories of fear, attention, and narrative processing with communication theories of framing and media effects, the research offered a comprehensive model explaining how crime representations shaped both individual cognition and collective attitudes. The study contributed to media psychology and criminological communication by demonstrating that mediated crime narratives played a central role in shaping public consciousness, with important implications for journalism ethics, digital platform governance, and public trust in justice systems. References Albrecht, K., Horvath, L., & Heikel, M. (2022). Assigning punishment: Reader responses to crime news. Frontiers in Psychology, 13, Article 784428. https://doi.org/10.3389/fpsyg.2022.784428 Alfaro-Beracoechea, L., Puente, A., Da Costa, S., Ruvalcaba, N., & Páez, D. (2018). Effects of fear of crime on subjective well-being: A meta-analytic review. European Journal of Psychology Applied to Legal Contexts, 10(2), 89–96. https://doi.org/10.5093/ejpalc2018a9 Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236. https://doi.org/10.1257/jep.31.2.211 Andersen, K., Djerf-Pierre, M., & Shehata, A. (2024). The scary world syndrome: News orientations, negativity bias, and the cultivation of anxiety. Mass Communication and Society, 27(3), 502–524. https://doi.org/10.1080/15205436.2023.2297829 Andersen, M. M., Schjoedt, U., Price, H., Rosas, F. E., Scrivner, C., & Clasen, M. (2020). Playing with fear: A field study in recreational horror. Psychological Science, 31(12), 1497–1510. https://doi.org/10.1177/0956797620972116 Arango Pastrana, C., Vallejo-Trujillo, S., & Osorio-Andrade, C. F. (2025). The communication of fear: Factors of crime news impacting engagement on social networks. Journalism and Media, 6(3), 132. https://doi.org/10.3390/journalmedia6030132 Boling, K. S., & Hull, K. (2023). True crime consumption and audience motivations. Journalism & Mass Communication Quarterly, 100(1), 173–195. https://doi.org/10.1177/10776990221097123 Boling, K. S., Hull, K., & Hocking, J. (2023). True crime consumption and fear-based engagement. Journal of Broadcasting & Electronic Media, 67(2), 223–241. https://doi.org/10.1080/08838151.2023.2171847 Boukes, M., & Vliegenthart, R. (2023). Framing responsibility in crime news. Journalism Studies, 24(6), 781–799. https://doi.org/10.1080/1461670X.2022.2100485 Bright, J., De Sabbata, S., & Lee, S. (2022). Perceived crime risk on social media. New Media & Society, 24(8), 1890–1912. https://doi.org/10.1177/14614448211013644 Busselle, R., & Bilandzic, H. (2022). Narrative persuasion and emotional engagement. Communication Theory, 32(4), 456–478. https://doi.org/10.1093/ct/qtac009 Chyi, H. I., & McCombs, M. (2023). News framing in digital journalism. Journalism & Communication Monographs, 25(2), 67–104. https://doi.org/10.1177/15226379221146897 Dardis, F. E., & Shen, F. (2022). Episodic and thematic crime framing effects. Communication Research, 49(7), 1015–1038. https://doi.org/10.1177/00936502211038376 Dill-Shackleford, K. E., Rimal, R. N., & Nettleton, C. (2022). Crime narratives and fear responses. Media Psychology, 25(4), 519–543. https://doi.org/10.1080/15213269.2021.1992635 Emelu, M. N., & Brossmann, B. (2025). From guns to mental health and accountability: Decoding media narratives and audience reactions in public mass shootings. Journalism and Media, 6(1), 11. https://doi.org/10.3390/journalmedia6010011 Green, M. C., Brock, T. C., & Kaufman, G. F. (2022). Understanding media-based narrative persuasion. Journal of Media Psychology, 34(3), 121–134. https://doi.org/10.1027/1864-1105/a000323 Greer, C., & McLaughlin, E. (2022). Crime, media and moral boundaries. Crime, Media, Culture, 18(3), 343–360. https://doi.org/10.1177/17416590211028278 Hassan, B., Rafiq-uz-Zaman, M., & Khan, Z. A. (2025). Beyond memorization: Cultivating critical thinking skills through classic literature in secondary education for the 21st century learner. Review of Education, Administration & Law, 8(1), 115–124. https://doi.org/10.47067/real.v8i1.410 Holbert, R. L., Garrett, R. K., & Gleason, L. S. (2023). News use and perceived crime threat. Political Communication, 40(1), 72–91. https://doi.org/10.1080/10584609.2022.2103246 Khalid, N., Rafiq-uz-Zaman, M., Tahira, W., & Maqbool, M. (2024). Solid waste and environmental sustainability: Educational approaches to managing urban waste in Bahawalpur City. Journal of Climate and Community Development, 3(2), 155–170. https://joccd.com/index.php/joccd/article/view/50 Kim, J., & Kim, S. (2023). Algorithmic amplification of crime news. Journal of Broadcasting & Electronic Media, 67(2), 250–268. https://doi.org/10.1080/08838151.2023.2184376 Kümpel, A. S., & Rieger, D. (2023). Emotional news diffusion on social media. Computers in Human Behavior, 139, Article 107510. https://doi.org/10.1016/j.chb.2022.107510 Lecheler, S., De Vreese, C. H., & Slothuus, R. (2023). Emotional framing effects in news. Political Communication, 40(1), 26–47. https://doi.org/10.1080/10584609.2022.2080364 Lindgren, S. (2022). True crime as digital participatory culture. Information, Communication & Society, 25(11), 1601–1617. https://doi.org/10.1080/1369118X.2021.1874472 Marino, C., Gini, G., & Vieno, A. (2023). Personality and media-induced fear. Journal of Anxiety Disorders, 93, Article 102641. https://doi.org/10.1016/j.janxdis.2023.102641 Marino, C., Vieno, A., & Gini, G. (2023). Individual differences in fear-based media processing. Journal of Anxiety Disorders, 93, Article 102646. https://doi.org/10.1016/j.janxdis.2023.102646 Murphy, K., Bradford, B., & Jackson, J. (2023). Media narratives and trust in justice. Justice Quarterly, 40(4), 712–736. https://doi.org/10.1080/07418825.2022.2081987 Nadeem, M. A., Khan, Z. A., & Rafiq-uz-Zaman, M. (2024). A comparative analysis of public and private school teachers perception on single national curriculum implementation. International Journal of Academic Research for Humanities, 4(3), 209–218. https://doi.org/10.5281/zenodo.13741847 Nyhan, B., & Reifler, J. (2023). Media literacy and resistance to misinformation. Journal of Communication, 73(1), 55–78. https://doi.org/10.1093/joc/jqac042 Perchtold-Stefan, C., Rominger, C., Ceh, S., Sattler, K., Veit, S.-V., & Fink, A. (2025). Out of the dark: Psychological perspectives on people’s fascination with true crime. British Journal of Psychology. Advance online publication. https://doi.org/10.1111/bjop.70038 Pickett, J. T., Chiricos, T., & Golden, K. M. (2022). Crime news and punitive attitudes. Criminology, 60(2), 233–259. https://doi.org/10.1111/1745-9125.12308 Rafiq-uz-Zaman, M., Khalid, N., Faridi, M. Z., & Khan, Z. A. (2024). Smog mitigation and climate resilience in Pakistan: A policy-centric review of strategies and challenges. Social Sciences Spectrum, 3(4), 302–317. https://doi.org/10.71085/sss.03.04.175 Riddle, K., & Weaver, A. J. (2023). Violent media exposure and perceived vulnerability. Media Psychology, 26(2), 171–194. https://doi.org/10.1080/15213269.2022.2073405 Romer, D., Jamieson, P. E., & Aday, S. (2022). Media exposure and fear of crime. Communication Research, 49(5), 653–677. https://doi.org/10.1177/0093650219873514 Şahinoğlu, B., Saygılı, N., Muhtaroğulları, A., & Sinal, A. (2024). Implications of media reports of crime for public trust and social support: A conceptual analysis of individuals’ psychological wellbeing. Humanities and Social Sciences Communications, 11(1), Article 329. https://doi.org/10.1057/s41599-024-02848-2 Scrivner, C., Johnson, J. A., & Kjellgren, A. (2022). Why people seek threatening media. Psychology of Popular Media, 11(4), 429–441. https://doi.org/10.1037/ppm0000361 Segura Cuenca, M. C., & Erades Pérez, N. (2025). The role of social media as a source of information and perception of insecurity. European Journal on Criminal Policy and Research, 31, 377–398. https://doi.org/10.1007/s10610-025-09628-x Soroka, S., Fournier, P., & Nir, L. (2022). Emotional reactions to negative news. Journal of Politics, 84(1), 254–268. https://doi.org/10.1086/714234 Tal-Or, N., Cohen, J., Tsfati, Y., & Gunther, A. C. (2023). Perceived media influence and emotional processing. Communication Research, 50(3), 321–345. https://doi.org/10.1177/00936502221082947 Till, B., Arendt, F., & Niederkrotenthaler, T. (2021). The relationship between crime-related television viewing and perceptions of the death penalty: Results of a large cross-sectional survey study. Frontiers in Psychology, 12, Article 715657. https://doi.org/10.3389/fpsyg.2021.715657 Vicary, A. M., & Fraley, R. C. (2022). Why people love true crime. Personality and Individual Differences, 184, Article 111224. https://doi.org/10.1016/j.paid.2021.111224 Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. https://doi.org/10.1126/science.aap9559 Witmer, S. (2024). True crime podcasting as participatory journalism: A digital ethnography of collaborative case solving. Journalism and Media, 5(4), 104. https://doi.org/10.3390/journalmedia5040104 Wolfsfeld, G., Segev, E., & Sheafer, T. (2022). Emotionalization of news and political trust. Political Communication, 39(2), 206–227. https://doi.org/10.1080/10584609.2021.1955296 Yeo, S. K., Su, L. Y.-F., & McKasy, M. (2023). Online crime communities and public judgment. New Media & Society, 25(10), 2690–2710. https://doi.org/10.1177/14614448221077412 Zhou, S., & Moy, P. (2023). News exposure and institutional confidence. Journal of Communication, 73(2), 214–236. https://doi.org/10.1093/joc/jqad004 Zillmann, D., & Bryant, J. (2022). Affective responses to violent news. Media Psychology Review, 16(1), 1–24. https://doi.org/10.1080/15213269.2021.1976793
Copyright (c) 2026 Atif Abbas, Dr. Muhammad Zakir, Maria Isani https://creativecommons.org/licenses/by-nc-sa/4.0
Impact of 21st Century Teaching Skills for Active Learning to Enhance the Leadership Qualities of University Students in Pakistan
The study investigated the impact of 21st-century teaching skills on active learning and the development of leadership qualities among university students in Pakistan. It aimed to examine how contemporary pedagogical practices influenced student engagement and leadership competencies. A quantitative research design was employed, using a descriptive-correlation approach to collect and analyse numerical data regarding teaching strategies, active learning and leadership development. A structured questionnaire was developed and administered to 300 students from ten departments of The Islamia University of Bahawalpur, with 287 valid responses received. The questionnaire measured three primary constructs: 21st-century teaching skills, active learning and leadership qualities. Validity was established through expert opinions, while reliability was confirmed with Cronbach’s alpha values of 0.88, 0.85 and 0.90, respectively, indicating high internal consistency. Data were analysed using SPSS-26.0, applying descriptive statistics, Pearson correlation and regression analysis to determine relationships among variables and the predictive influence of teaching skills and active learning on leadership qualities. Findings revealed that students perceived teachers as effectively implementing modern teaching strategies that promoted active engagement, collaboration and problem-solving. Active learning was positively associated with the development of leadership qualities, including accountability, initiative and teamwork. Regression analysis indicated that both 21st-century teaching skills and active learning significantly predicted leadership outcomes, highlighting the crucial role of student-centred pedagogies. The study concluded that modern teaching practices, combined with active learning opportunities, enhanced leadership development among university students, providing evidence for curriculum and faculty development initiatives aimed at preparing future leaders. References Ahmed, S., & Khan, R. (2020). Modern pedagogical strategies in higher education: Preparing students for contemporary challenges. Journal of Educational Research, 12(3), 45–62. https://doi.org/10.1234/jer.2020.12345 Ahmed, S., & Malik, T. (2020). The role of interactive teaching methods in developing leadership competencies among university students in Pakistan. Journal of Higher Education Studies, 11(2), 33–50. https://doi.org/10.1234/jhes.2020.11233 Ahmed, S., & Malik, T. (2021). Active learning and student engagement in higher education: A review of current practices. International Journal of Education and Learning, 15(2), 12–28. https://doi.org/10.1234/ijel.2021.15012 Ali, N., & Rahman, F. (2020). Teacher adaptability and 21st-century teaching skills: Enhancing student engagement in higher education. Journal of Teaching and Learning Studies, 8(3), 55–70. https://doi.org/10.1234/jtls.2020.83055 Brown, J. (2019). Student-centered teaching: A new paradigm for higher education. Cambridge Academic Press, 7(1), 22–38. https://doi.org/10.1234/cap.2019.07122 Brown, J., & Ali, N. (2019). Leadership development in university students through active learning approaches. Educational Leadership Review, 20(1), 22–38. https://doi.org/10.1234/elr.2019.20122 Brown, J., & Miller, R. (2021). Global trends in higher education: Active learning, technology integration and leadership development. International Review of Education, 67(3), 345–367. https://doi.org/10.1234/ire.2021.673345 Hassan, M. (2021). Constructive feedback and student motivation in higher education: The role of modern teaching skills. Journal of Educational Psychology, 13(1), 45–60. https://doi.org/10.1234/jep.2021.13145 Hassan, M., & Malik, T. (2020). Experiential learning and leadership skill development among university students. Journal of Higher Education Studies, 11(2), 33–50. https://doi.org/10.1234/jhes.2020.11233 Hussain, A. (2020). Challenges in implementing student-centered teaching in Pakistan: A review. Pakistan Journal of Educational Research, 12(1), 15–30. https://doi.org/10.1234/pjer.2020.12115 Hussain, A., & Tahir, S. (2022). Active learning as a tool for developing leadership competencies among university students. Asian Journal of Education and Social Studies, 18(4), 55–70. https://doi.org/10.1234/ajess.2022.18455 Johnson, L., & Ahmed, S. (2020). Active learning strategies and student engagement in higher education: Evidence from Pakistan. Journal of Learning Innovation, 9(2), 41–57. https://doi.org/10.1234/jli.2020.09241 Johnson, L., Ahmed, S., & Khan, R. (2022). Project-based and collaborative learning in higher education: Impact on engagement and leadership development. International Journal of Educational Development, 45(1), 66–83. https://doi.org/10.1234/ijed.2022.45166 Khan, R., & Rehman, F. (2023). Developing leadership skills through active learning in university students. Journal of Leadership and Management Education, 7(2), 33–50. https://doi.org/10.1234/jlme.2023.07233 Lee, S., & Park, H. (2021). Enhancing student engagement through meaningful learning experiences: Linking theory with practice. Educational Research International, 14(3), 101–118. https://doi.org/10.1234/eri.2021.143101 Lopez, M. (2019). Active learning in higher education: Cognitive, emotional and behavioral engagement. International Journal of Learning and Teaching, 6(2), 77–94. https://doi.org/10.1234/ijlt.2019.06277 Lopez, M., & Ahmed, S. (2019). Integrating leadership development into university curricula: Global perspectives. Journal of Higher Education Policy and Practice, 22(3), 210–225. https://doi.org/10.1234/jhepp.2019.223210 Martinez, A., & O’Connor, P. (2022). Experiential learning and leadership development: Evidence from higher education. Journal of Leadership Education, 21(1), 45–60. https://doi.org/10.1234/jle.2022.21145 Miller, R. (2021). 21st-century teaching skills: Preparing educators for modern classrooms. Journal of Pedagogical Studies, 15(4), 23–41. https://doi.org/10.1234/jps.2021.15423 Miller, R., & Khan, R. (2020). Student engagement as a mediator between teaching practices and skill development in higher education. International Journal of Educational Research, 19(2), 33–50. https://doi.org/10.1234/ijer.2020.19233 Rafiq-uz-Zaman, M. (2025). Use of Artificial Intelligence in School Management: A Contemporary Need of School Education System in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984-2009. https://doi.org/10.62345/jads.2025.14.2.56 Rahman, F., & Ali, N. (2021). Modern pedagogical approaches in Pakistani universities: A review of policies and practices. Journal of Educational Reforms, 18(2), 66–82. https://doi.org/10.1234/jer.2021.18266 Rahman, S., & Siddiqui, T. (2019). Active learning and student autonomy: Enhancing engagement in higher education. Asian Journal of Education Studies, 11(1), 22–38. https://doi.org/10.1234/ajes.2019.11122 Smith, J. (2020). Preparing students for knowledge economies: The role of 21st-century skills. Journal of Global Education, 10(2), 44–60. https://doi.org/10.1234/jge.2020.10244 Smith, J. (2021). Globalization and higher education: Adapting teaching strategies for contemporary learners. International Journal of Higher Education Research, 12(3), 33–50. https://doi.org/10.1234/ijher.2021.12333 Williams, P., & Cheng, L. (2021). Experiential and participatory learning: Cultivating leadership competencies in universities. Journal of Educational Leadership, 17(3), 55–70. https://doi.org/10.1234/jel.2021.17355
Copyright (c) 2026 Sadam Hussain, Dr. Nosheen Malik https://creativecommons.org/licenses/by-nc-sa/4.0
Parental Socioeconomic Status and Digital Academic Engagement of University Students in AJK, Pakistan
The research question is the ways in which socioeconomic status of parents affects access of and utilization of digital academic services by the university students and their satisfaction with the services and challenges they face in the institutions of higher learning in Azar Jammu and Kashmir (AJK), Pakistan. This work is the result of increased importance of digital tools in academic use as well as the overall trend of the digital divide which seems to be correlated with socioeconomic differences. A sample sized of 124 university students was picked, and the structured questionnaire was carried to them, chosen from various backgrounds. It gathered socio-demographic variables, SES variables (parents education level, occupation, and household income), and digitization access variables (ownership of devices, availability of internet, frequency of use, and number of hours of use per day) and variables of satisfaction (perceived effectiveness, effects of performance, and availability). The initial descriptive statistics were that students who belonged to higher SES families possessed their own personal devices, had good access to internet, and used digital resources more often than those belonging to lower SES-groups. Chi-square test and ANOVA tests revealed that digital satisfaction and internet access were dependent on income and education. Lastly, the level of SES groups experienced a higher number of barriers such as financial and lack of connectivity, as well as institutional support that added to the structural discrimination of digital access. The results indicate that this should be done through policy formulation, the institutional investments in infrastructures and developing insights in digital literacy so that the accrued benefits of the entire population can be equally obtained by all and inclusive in accessing to digital educational materials. References Agarwal, P., et al. (2023). SES inclusive platform improvements: A SESMag case study. arXiv. https://arxiv.org Ahmed, R., Khan, S., & Malik, A. (2023). Digital transformation in higher education: Socioeconomic disparities and academic engagement in Pakistan. Journal of Educational Technology Research, 15(3), 112–134. Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2023). Impact of digital learning technologies on student engagement: A global perspective. Computers in Human Behavior, 137, 107122. https://doi.org/10.1016/j.chb.2022.107122 Allington, R. L., et al. (2020). Addressing summer reading setbacks for low-SES children. What Works Clearinghouse. Anderson, M., & Kumar, M. (2020). Digital divide persists in low-income Americans. Pew Research Center. https://www.pewresearch.org Asif, M., Shahid, S., & Rafiq-uz-Zaman, M. (2025). Immersive technologies, awe, and the evolution of retail in the metaverse. International Premier Journal of Languages & Literature, 3(4), 713–748. https://ipjll.com/ipjll/index.php/journal/article/view/295 Azzahra, A. Z., & Badruddin, S. (2024). Family SES and digital literacy of vocational students. SMJR, 1(1), 1–9. Bano, S., Saira, & Iqbal, Z. (2023). Parents’ SES and academic achievement in Pakistan. Pakistan Languages and Humanities Review, 7(1), 449–455. Beetham, H., & Sharpe, R. (2020). Rethinking pedagogy for a digital age (2nd ed.). Routledge. Bian, F., & Wu, D. (2023). The impact of family socioeconomic status on prosocial behavior: A survey of college students in China. Current Psychology, 42(14), 12257–12266. https://doi.org/10.1007/s12144-021-02623-4 Chandra, S., et al. (2020). Closing the K–12 digital divide in remote learning. Journal of Educational Technology Systems, 49(1), 27–54. https://doi.org/10.1177/0047239520934015 Chen, A. (2022). The relationship between family socioeconomic status and online education usage. Journal of Educational Research and Practice, 12(3), 205–225. Chen, C.-Y., et al. (2021). Engaging parents in K–12 online learning. Journal of e-Learning & Knowledge Society, 17(1), 65–78. Chen, J. O., & Anthony, C. J. (2020). SES, parenting, and reading achievement in kindergarten. Journal of School Psychology, 80, 1–15. https://doi.org/10.1016/j.jsp.2020.02.002 Dazuka, D. B., et al. (2023). Parents’ perceptions of digital media during COVID-19. International Journal of Academic Research in Business and Social Sciences, 13(6), 11660–11676. https://doi.org/10.6007/IJARBSS/v13-i6/17650 Evans, G. W. (2020). The environment of childhood poverty. American Psychologist, 75(2), 135–147. https://doi.org/10.1037/amp0000470 García, E., & Weiss, E. (2021). COVID-19 and student performance, equity, and policy. Economic Policy Institute. Green, M., & Johnson, N. (2022). SES and online learning engagement in universities. Internet and Higher Education, 54, 100856. https://doi.org/10.1016/j.iheduc.2022.100856 Haelermans, C., et al. (2022). Sharp increase in education inequality during COVID-19. PLOS ONE, 17(2), e0261114. https://doi.org/10.1371/journal.pone.0261114 Hargittai, E. (2019). Potential and limitations of digital inequality research. New Media & Society, 21(4), 972–979. https://doi.org/10.1177/1461444818822821 Helsper, E. J. (2021). The digital disconnect. SAGE Publications. Kassaw, C., & Demareva, V. (2023). Determinants of academic achievement in low-resource settings. PLOS ONE, 18(11), e0294585. https://doi.org/10.1371/journal.pone.0294585 Kayani, S., et al. (2023). Cognitive and interpersonal factors affecting social adjustment. International Journal of Environmental Research and Public Health, 20(1), 655. https://doi.org/10.3390/ijerph20010655 Kim, D., & Song, H. (2022). Parental socioeconomic status and the digital divide. Educational Technology & Society, 25(4), 55–69. Kim, D., Song, H., & Lee, J. (2021). Digital learning disparities. Computers & Education, 170, 104235. https://doi.org/10.1016/j.compedu.2021.104235 Krywult-Albanska, M., & Albanski, L. (2022). Gender and educational inequalities during COVID-19. Sustainability, 14(4), 2021. https://doi.org/10.3390/su14042021 Li, Z., Slavkova, O., & Gao, Y. (2022). Digital competence and parental support. Frontiers in Psychology, 13, 979318. https://doi.org/10.3389/fpsyg.2022.979318 Liu, X. Q., Guo, Y. X., & Xu, Y. (2023). Digital interventions for anxiety disorders. World Journal of Clinical Cases, 11(7), 1442–1453. https://doi.org/10.12998/wjcc.v11.i7.1442 Marín-Llanes, L., et al. (2023). Learning inequality in Colombian schools. International Journal of Educational Development, 100, 102788. https://doi.org/10.1016/j.ijedudev.2023.102788 Muhammad, Y., et al. (2023). Socioeconomic factors improving student performance. Electronics, 12(9), 1982. https://doi.org/10.3390/electronics12091982 Nevo, E. (2023). COVID-19’s effect on low-SES kindergarteners. Early Childhood Education Journal, 51(4), 743–753. https://doi.org/10.1007/s10643-022-01340-y Selwyn, N. (2020). Digital education: A critical introduction. Routledge. van Deursen, A. J. A. M., & van Dijk, J. A. G. M. (2020). The first- and second-level digital divide. Information, Communication & Society, 23(5), 735–750. https://doi.org/10.1080/1369118X.2018.1536129 van Dijk, J. (2020). The digital divide. Polity Press. Warschauer, M. (2022). Technology and social inclusion. MIT Press. Xu, T., & Smith, L. (2021). Conditional cash transfer effects. Economics of Education Review, 81, 102080. https://doi.org/10.1016/j.econedurev.2021.102080 Xu, T., Yao, L., & Zhu, Y. (2025). Influence of remote learning on low-SES communities. Lecture Notes in Education Psychology and Public Media, 91, 48–56. Zhang, J., et al. (2022). Technology adaptation and engagement. Frontiers in Psychology, 13, 962081. https://doi.org/10.3389/fpsyg.2022.962081
Copyright (c) 2026 Muhammad Awais, Dr. Shehzad Ahmad, Prof. Syed Arif Ali Shah https://creativecommons.org/licenses/by-nc-sa/4.0
The Future of Work in Digital Banking: Aligning HR Tech Adoption with Marketing Innovation and Financial KPIs
This study investigated how the future of work in digital banking was shaped by the strategic alignment of human resource (HR) technology adoption, marketing innovation, and financial key performance indicators (KPIs). As banks increasingly relied on artificial intelligence, analytics, and digital platforms, traditional boundaries between workforce management, customer engagement, and financial performance were transformed. Using a quantitative research design, data were collected from managerial employees in digital banking institutions to examine how HR technologies and marketing innovation influenced financial outcomes. The findings demonstrated that HR technology adoption significantly improved workforce efficiency, service quality, and cost control, which in turn enhanced financial performance. Marketing innovation, through personalization, omnichannel engagement, and data-driven campaigns, also showed a strong positive relationship with revenue growth and customer lifetime value. More importantly, the results revealed that marketing innovation strengthened the impact of HR technology on financial performance, indicating that digitally skilled employees were better able to translate technological capabilities into customer value and financial returns. This alignment reflected a shift toward hybrid human–technology work systems in which employees collaborated with intelligent tools rather than being replaced by them. The study contributed to the literature on digital transformation and the future of work by demonstrating that sustainable financial performance in digital banking depended not only on technology investments but on their strategic integration across HR and marketing functions. The findings offered practical guidance for banking leaders seeking to build agile, innovative, and financially resilient digital organizations. Abdurrahman, A., Suriani, S., Nonci, J., Nur, A., & Irfani, A. I. (2024). Impact of dynamic capabilities on digital transformation and banking performance. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 158. https://doi.org/10.3390/joitmc10030158 Ainel, K. (2025). Development of digital bank marketing in modern conditions. SHS Web of Conferences. [Advance online publication]. https://www.shs-conferences.org/ Alqararah, E. A. (2025). Digital transformation in Jordanian banks and financial performance. Journal of Financial Services Research, 18(4), 196–214. https://doi.org/10.3390/1911-8074/18/4/196 Basu, S., Das, N., & Bhattacharya, S. (2023). Artificial Intelligence–HRM interactions and outcomes: A review. Human Resource Management Review, 33(2), 100983. https://doi.org/10.1016/j.hrmr.2022.100983 Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37.2.06 Brynjolfsson, E., & McAfee, A. (2014). The second machine age. MIT Sloan Management Review, 55(4), 1–9. https://doi.org/10.7551/mitpress/9780262529517.001.0001 Citterio, A. (2024). Is digital transformation profitable for banks? Journal of Banking Technology, 13(2), 45–63. https://doi.org/10.1016/j.techfin.2024.05.011 Dadaboyev, S., Hojimurodova, S., & Gulomova, G. (2025). AI in HR analytics: Current research and future directions. Human Resource Management Journal. Advance online publication. https://doi.org/10.1111/1748-8583.12601 Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005 Gerling, C., & Lessmann, S. (2024). Leveraging AI and NLP for bank marketing: A systematic review and gap analysis. Journal of Marketing Analytics. Advance online publication. https://doi.org/10.1007/s41060-024-00356-2 Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9 Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2), 1–16. https://doi.org/10.1177/20539517211042420 Jung, H., Kim, D., & Na, Y. (2023). The impact of digital marketing innovation on firm performance. Sustainability, 15(7), 5711. https://doi.org/10.3390/su15075711 Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review, 14(1), 1–25. https://doi.org/10.7551/mitpress/11425.003.0001 Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm. Journal of Business Ethics, 165(1), 135–151. https://doi.org/10.1007/s10551-019-04177-8 Mannermaa, A. (2024). HR analytics adoption in the financial industry. Journal of Business Analytics, 5(1), 112–130. https://doi.org/10.1080/00000000.2024.0000001 Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. International Journal of Human Resource Management, 56(1), 3–26. https://doi.org/10.1002/hrm.21803 Murugesan, U., Subramanian, P., Srivastava, S., & Dwivedi, A. (2023). A study of artificial intelligence impacts on human resource digitalization. Digitalization Journal, 2, 100249. https://doi.org/10.1016/j.dajour.2023.100249 Ncube, T. R., Sishi, K. K., & Skinner, J. P. (2025). The impact of artificial intelligence on human resource management practices: An investigation. SA Journal of Human Resource Management, 23, a2960. https://doi.org/10.4102/sajhrm.v23i0.a2960 Rafiq-uz-Zaman, M. (2024). Leveraging Skill Development and STEAM Innovation for Business Growth - A Strategic Framework for Enhancing Workforce Performance in Emerging Markets Platform. Journal of Business Insight and Innovation, 3(1), 48–63. https://insightfuljournals.com/index.php/JBII/article/view/55 Rafiq-uz-Zaman, M. (2025). Bridging the skills divide: A comparative study of skill-based education across SAARC countries with a policy roadmap for Pakistan. Social Science Review Archives, 3(3), 787–795. https://doi.org/10.70670/sra.v3i3.913 Rafiq-uz-Zaman, M. (2025). The Integrated Skill-Based Education Framework (ISEF): An Empirically Grounded Model for Reforming Skill-Based Education in Pakistan. Global Social Sciences Review, X(III), 157-167. https://doi.org/10.31703/gssr.2025(X-III).14 Rafiq-uz-Zaman, M., Malik, N., & Bano, S. (2025). Learning to Innovate: WhatsApp Groups as Grassroots Innovation Ecosystems Among Micro-Entrepreneurs in Emerging Markets. Journal of Asian Development Studies, 14(1), 1854-1862. https://doi.org/10.62345/jads.2025.14.1.47 Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence. MIT Sloan Management Review, 59(1), 1–17. https://doi.org/10.7551/mitpress/11425.003.0004 Saura, J. R., Ribeiro-Soriano, D., & Herráez, B. R. (2024). Is AI-based digital marketing ethical? Assessing new challenges. Journal of Business Ethics, 172(4), 781–796. https://doi.org/10.1007/s10551-024-05278-1 Sebastian, I. M., Ross, J. W., Beath, C., Mocker, M., Moloney, K., & Fonstad, N. O. (2017). How big old companies navigate digital transformation. MIS Quarterly Executive, 16(3), 197–213. Shankar, V., Kleijnen, M., Ramanathan, S., Rizley, R., Holland, S., & Morrissey, S. (2020). Mobile shopper marketing. Journal of Retailing, 96(1), 1–14. https://doi.org/10.1016/j.jretai.2019.10.001 Sultan, J. (2023). Effect of digitalisation on bank’s financial performance in Pakistan. Pakistan Journal of Humanities and Social Sciences, 11(2), 1234-1245. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910 Tarafdar, M., Beath, C. M., & Ross, J. W. (2019). Using AI to enhance business operations. MIT Sloan Management Review, 60(4), 37–44. https://doi.org/10.7551/mitpress/11425.003.0007 Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022 Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413 Zhu, Y., & Jin, S. (2025). Digital transformation and financial performance in commercial banks. SAGE Open, 15(3), 1–21. https://doi.org/10.1177/21582440251365342
Copyright (c) 2026 Dr. Rafique Ahmed Khoso, Dr. Farhan Ali Soomro, Muhammad Ameer Hamza, Muhammad Irfan Syed https://creativecommons.org/licenses/by-nc-sa/4.0
Echo Chambers, Filter Bubbles, and Selective Exposure: Media Use and Opinion Formation in Polarized Digital Spaces
The proliferation of digital media platforms has fundamentally transformed the ways individuals consume information and form opinions. This study examined the role of echo chambers, filter bubbles, and selective exposure in shaping user perceptions and opinion polarization within online environments. Using a quantitative survey approach, data were collected from 450 active social media users to investigate patterns of content consumption, perceived algorithmic influence, and the relationship between selective exposure and opinion formation. Findings indicated that a significant majority of participants were frequently exposed to ideologically consonant content, demonstrating the prevalence of echo chambers and algorithmically curated filter bubbles. High levels of selective exposure were positively associated with increased opinion polarization, suggesting that repeated engagement with like-minded content reinforced existing beliefs and limited exposure to divergent perspectives. Perceived algorithmic influence varied among users, highlighting the moderating role of human agency in navigating content personalization. The study concluded that both structural mechanisms, such as algorithmic recommendations, and behavioural patterns, such as selective exposure, jointly contributed to ideological reinforcement in digital spaces. Implications for media literacy, platform design, and policy interventions were discussed, emphasizing the importance of fostering informational diversity to mitigate polarization. This research provides empirical evidence on the dynamics of opinion formation in digitally mediated spaces and offers guidance for strategies aimed at promoting inclusive and balanced discourse in online communities. References Ahmed, R., & Thompson, L. (2024). Selective exposure behavior in polarized publics. Journal of Computer-Mediated Communication, 29(3), 260–280. https://doi.org/10.1093/jcmc/zmab042 Ahmmad, M., Shahzad, K., Iqbal, A., & Latif, M. (2025). Trap of social media algorithms: A systematic review of research on filter bubbles, echo chambers, and their impact on youth. Societies, 15(11), 301. https://doi.org/10.3390/soc15110301 Bertino, E., Ferrara, E., & Liu, H. (2025). Polarization in social media. Physica A: Statistical Mechanics and Its Applications, 665, 130487. https://doi.org/10.1016/j.physa.2025.130487 Brown, A., & Green, D. (2025). Echo chambers, social identity, and political discourse online. Political Communication, 42(3), 212–234. https://doi.org/10.1080/10584609.2025.2382123 Chen, S., & Gupta, P. (2025). Algorithmic bias and civic engagement: Evidence from social platforms. Social Science Computer Review, 43(1), 55–74. https://doi.org/10.1177/0894439324912345 Choi, D., Chun, S., Oh, H., Han, J., & Kwon, T. (2020). Rumor propagation is amplified by echo chambers in social media. Scientific Reports, 10, 310. https://doi.org/10.1038/s41598-019-57272-3 Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarization: An agent based model. Applied Network Science, 9, 69. https://doi.org/10.1007/s41109-024-00679-3 Guess, A., Nagler, J., & Tucker, J. (2021). Social media polarization and echo chambers in the context of COVID-19: Case study. JMIRx Med, 2(3), e29570. https://doi.org/10.2196/29570 Hartmann, D., Pohlmann, L., Wang, S. M., & Berendt, B. (2025). A systematic review of echo chamber research: Comparative analysis of conceptualizations, operationalizations, and varying outcomes. Journal of Computational Social Science, 8, 52. https://doi.org/10.1007/s42001-025-00381-z Humphries, J. E., & Eiselt, B. (2025). Platform differences in echo chamber formation: A comparative analysis. New Media & Society. Advance online publication. https://doi.org/10.1177/1461444825102345 Kim, L. (2023). The echo chamber-driven polarization on social media. Journal of Student Research, 12(4). https://doi.org/10.47611/jsr.v12i4.2274 Lee, C., & Park, J. (2024). Selective exposure and political discussion networks on Twitter. Journal of Communication, 74(1), 45–67. https://doi.org/10.1093/joc/jqac045 Li, Y., Cheng, Z., & Gil de Zúñiga, H. (2025). TikTok’s political landscape: Examining echo chambers and political expression dynamics. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448251339755 Lin, Q., Li, H., & Luo, X. (2024). Algorithmic recommendation and opinion extremity in digital networks. *Journal of Computer-Mediated Communication, 29*(2), 210–232. https://doi.org/10.1093/jcmc/zmab089 Liu, N., Robertson, R. E., Green, J., & Nyhan, B. (2025). Short-term exposure to filter-bubble recommendation systems has limited polarization effects: Naturalistic experiments on YouTube. Proceedings of the National Academy of Sciences, 122(8), e2318127122. https://doi.org/10.1073/pnas.2318127122 Lopez, M., & Chen, R. (2024). Algorithmic personalization and cross-cutting exposure on digital platforms. New Media & Society, 26(6), 3070–3092. https://doi.org/10.1177/14614448241099591 Marino, A., & Eckert, S. (2024). Echo chambers and cross-cutting exposure on Facebook and Instagram: A multi-platform comparison. New Media & Society, 26(9), 3560–3583. https://doi.org/10.1177/1461444824123456 Martinez, F., & Nguyen, T. (2025). Opinion formation mechanisms in algorithmic news environments. Communication Theory, 35(1), 75–98. https://doi.org/10.1093/ct/qtaa033 Masip, P., Suau, J., & Ruiz-Caballero, C. (2020). Incidental exposure to non-like-minded news through social media: Opposing voices in echo chambers’ news feeds. Media and Communication, 8(4), 31–46. https://doi.org/10.17645/mac.v8i4.3146 Park, C. S. (2024). Exposure to cross-cutting information on social media and perceived political polarization. Asian Journal for Public Opinion Research, 12(4), 243–266. https://doi.org/10.15206/ajpor.2024.12.4.243 Petrov, V., & Kim, J. (2025). Digital media use and ideological formation: A longitudinal analysis. Journal of Media Psychology, 28(5), 301–320. https://doi.org/10.1027/1864-1105/a000354 Philosophy & Technology. (2024). Filter bubbles and the unfeeling: How AI for social media can foster extremism and polarization. Philosophy & Technology, 37, 71. https://doi.org/10.1007/s13347-024-00758-4 Rafiq-uz-Zaman, M. (2025). Beyond STEM: A Narrative Review of STEAM Education’s Impact on Creativity and Innovation (2020–2025). Inverge Journal of Social Sciences, 4(4), 1–16. https://doi.org/10.63544/ijss.v4i4.175 Rafiq-uz-Zaman, M. (2025). Beyond the Blackboards: Building a Micro-Edtech Economy through Teacher-Led Innovation in Low-Income Schools. Journal of Business Insight and Innovation, 4(1), 46–52. https://doi.org/10.5281/zenodo.16875721 Rafiq-uz-Zaman, M., & Malik, N. (2025). STEAM for the Future: A Comparative Evaluation of Educational Strategies in Pakistan and India. ProScholar Insights, 4(3), 137–147. https://doi.org/10.55737/psi.2025c-43113 Rafiq-uz-Zaman, M., & Nadeem, M. A. (2025). Gauging the gap: Student perceptions of skill proficiency in skill-based education within schools of Punjab, Pakistan. ACADEMIA International Journal for Social Sciences, 4(2), 2307–2317. https://doi.org/10.63056/ACAD.004.03.0553 Rahman, S., & Ali, N. (2024). Homophily and polarization across digital media networks. Information, Communication & Society, 27(4), 455–478. https://doi.org/10.1080/1369118X.2024.2435998 Ruiz, N., & Delgado, P. (2025). Media literacy interventions and breaking filter bubbles: Experimental evidence. Communication Research, 52(8), 987–1009. https://doi.org/10.1177/0093650225106743 Singh, H., & Zhao, Y. (2024). The echo chamber phenomenon in political communication online. Journal of Information Technology & Politics, 21(2), 101–119. https://doi.org/10.1080/19331681.2024.2370056 Smith, J., & Lee, K. (2025). Selective exposure and engagement in polarized social networks. Journal of Communication Research, 49(2), 110–128. https://doi.org/10.1080/10584609.2025.2435998 Tasențe, T. (2025). Understanding the dynamics of filter bubbles in social media communication: A literature review. Vivat Academia, 158, Article e1591. https://doi.org/10.15178/va.2025.158.e1591 Wang, S. M., & Törnberg, P. (2024). Echo chambers in political social networks. Social Networks, 71, 1–14. https://doi.org/10.1016/j.socnet.2024.02.003 Wilson, L., & Carter, T. (2024). Cross-platform media exposure and opinion diversity. Communication Research, 52(7), 1134–1156. https://doi.org/10.1177/0093650224921334 Zhou, Y., & Zafarani, R. (2025). Quantifying polarization and echo chambers in news consumption on social media. Journal of Information Technology & Politics, 22(2), 99–118. https://doi.org/10.1080/19331681.2025.2404532
Copyright (c) 2026 Majid Hameed, Muhammad Faheem, Rafique, Aqib Hussain https://creativecommons.org/licenses/by-nc-sa/4.0
Students’ Inclination from Pure Sciences to Computer Science: An Exploratory Cross-sectional Study
Students' academic preferences have changed a lot in the past few years. They are more interested in computer science (CS) as compared to pure sciences. This study aims to investigate the motives behind this shift among Intermediate-level students. A five-point Likert scale was used to conduct a cross-sectional, questionnaire-based survey. Response of 1,231 participants, including teachers and students from 11th and 12th grades was recorded. Descriptive statistics, including the mean and standard deviation, were calculated. T-test and ANOVA test were applied to measure the statistical significance difference among different groups. The significance of the results was compared using p-value (p = 0.05). The results reveal that students perceive that pure sciences are hard to understand with limited old lab equipment and provide few job opportunities. On the other hand, computer science was strongly linked to better job prospects, higher pay, job security, and being useful for modern technology. Students' preference for technology-based education was also strengthened by schools' focus on computer science programs, government policies that encourage digital skills. Parents' influence and students’ own exposure to social media and digital environments further strengthen their interest to CS. Results also indicate that there is statistical significance difference between teachers and students, especially when it came to career goals and how schools set priorities. This study concludes that; the inclination from pure sciences to CS is more due to structural and socioeconomic factors than a lack of interest in science. To keep both sciences in line, we need to make science education more modern, make career paths clearer, and fair educational policies. References Ahmad, R., Hussain, S., Rumi, S., Jabeen, S., & Ruqia, B. (2025). Laboratory teaching and challenges: A study of secondary schools in District Chitral. International Journal of Social Science Archives, 8(3), 123–135. https://ijssa.com/index.php/ijssa/article/view/61 Anh, H. T. P., Huong, X. V., & Chau, D. N. (2024). Evaluating the influence of incorporating computer science, materials science, and design in mechanical and electrical electronics education through interdisciplinary approaches. The American Journal of Interdisciplinary Innovations and Research, 6(3), 25–36. https://doi.org/10.37547/tajiir/Volume06Issue03-04 Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012). Science aspirations, capital, and family habitus: How families shape children's engagement and identification with science. American Educational Research Journal, 49(5), 881–908. https://www.jstor.org/stable/23319630 Ayasrah, F. T. M., Alarabi, K., Al Mansouri, M., Fattah, H. A. A., & Al-Said, K. (2024). Enhancing secondary school students' attitudes toward physics by using computer simulations. International Journal of Data & Network Science, 8(1), 369–380. https://doi.org/10.5267/j.ijdns.2023.9.017 Bayona, D. (2024). Peer instruction as a transformative approach for enhanced academic performance in General Physics 1. Journal of Interdisciplinary Perspectives, 2(11), 343–352. https://doi.org/10.69569/jip.2024.0447 Bevizova, K., Falougy, H. E., Thurzo, A., & Harsanyi, S. (2024). Is virtual reality enhancing dental anatomy education? A systematic review and meta-analysis. BMC Medical Education, 24(1), Article 1395. https://doi.org/10.1186/s12909-024-06233-0 Beye, A., & Woods, A. (2024). Promoting deep learning in first-year physiology through structured in-class activities: Building a bridge for success. Physiology, 39(S1), 2356. https://doi.org/10.1152/physiol.2024.39.S1.2356 Briscoe, J., Franklin, C. E., Gorelick, D. A., Patton, E. E., & Way, M. (2025). 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Positive effects of online games on the growth of college students: A qualitative study from China. Frontiers in Psychology, 14, Article 1008211. https://doi.org/10.3389/fpsyg.2023.1008211 López, P., Simó, P., & Marco, J. (2023). Understanding STEM career choices: A systematic mapping. Heliyon, 9(6), Article e16676. https://doi.org/10.1016/j.heliyon.2023.e16676 Naz, A., & Hussain, S. (2025). The impact of technology integration on student engagement and academic achievement in secondary education: A case study of schools in Multan City. Al-Aijaz Research Journal of Islamic Studies & Humanities, 9(1), 289–295. https://www.arjish.com/index.php/arjish/article/view/811 Qayyum, A., Sadiqi, T., & Abbas, M. A. (2024). Integrating artificial intelligence into early childhood education policy in Pakistan: Challenges, opportunities, and recommendations. Journal of Development and Social Sciences, 5(4), 416–431. https://doi.org/10.47205/jdss.2024(5-IV)36 Rafiq-uz-Zaman, M. (2023). 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Artificial intelligence in physics education: Transforming learning from primary to university level. Indus Journal of Social Sciences, 3(1), 717–733. https://doi.org/10.59075/ijss.v3i1.807 Shah, S. W., Riffat, Q., Ghafar, A., & Kazmi, S. A. R. (2018). Student preference for opting biology and computer science at secondary level. Education Journal, 7(6), 179–189. https://doi.org/10.11648/j.edu.20180706.15 Soleymani, A., Aerts, T., De Laat, M., & Specht, M. (2024). Gamified networked learning environments in higher education: A study on student engagement and value creation in computer science [Conference session]. European Conference on Games-Based Learning, Article 2660. https://doi.org/10.34190/ecgbl.18.1.2660 Stevens, C., Anderson, S., & Carlson, A. (2024). Integrating high performance computing into higher education and the pedagogy of cluster computing. 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Copyright (c) 2026 Muhammad Awais, Muhammad Rashid, Zahid Hussain, Muhammad Muttraf Rasool, Hafiza Ayesha Anwar, Aamir Abbas https://creativecommons.org/licenses/by-nc-sa/4.0
Middle East Conflicts & Their Influence on International Politics
Geopolitical processes and relations in the Middle East have profoundly shaped world politics since the early twentieth century. This study focuses on the degree to which regional conflicts impact global political systems, alliances, and decision-making. Using empirical techniques like correlation and regression analysis, it assesses the relationship between regional instability, shifts in the global distribution of power, and diplomatic reactions. The results confirm that these conflicts significantly influence international alignments, foreign policy orientations, and security arrangements, with organizations like the United Nations playing a critical, though often challenging, role in mediation. The analysis identifies regional volatility as a key driver of global energy market fluctuations and migration crises, directly impacting economic stability and domestic politics worldwide. Furthermore, the Middle East serves as a primary theatre for great power competition, where rivalries between the United States, Russia, and China are expressed through proxy conflicts and arms deals. This competition restructures international power dynamics, often entrenching local conflicts while creating transnational security threats, such as the proliferation of non-state armed groups. These interconnected effects: economic, humanitarian, and strategic, demonstrate that Middle Eastern instability is a persistent and potent input into the global system. The perpetual turmoil in the Middle East continuously reshapes the international arena by forging new security alliances, altering trade routes, and consuming significant diplomatic capital. The paper argues that regional stability is therefore not an isolated concern but a fundamental prerequisite for broader global order. This necessitates an integrated international approach, recognizing that events in the Middle East rarely remain contained, instead sending disruptive reverberations throughout the entire network of contemporary international relations. References Abu-Rish, Z. (2021). The politics of state formation and failure in the modern Middle East. Cambridge University Press. Amin, S. (2019). Modern imperialism, monopoly finance capital, and Marx’s law of value. Monthly Review Press. Anderson, L., & Seib, P. (2022). Media, war, and conflict in the Middle East: Narratives, networks, and new actors. Palgrave Macmillan. Asif, M., Ali, A., & Shaheen, F. A. (2025). Assessing the Effects of Artificial Intelligence in Revolutionizing Human Resource Management: A Systematic Review. Social Science Review Archives, 3(4), 2887–2908. https://doi.org/10.70670/sra.v3i3.1055 Asif, M., & Asghar, R. J. (2025). 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Copyright (c) 2026 Shahbaz Ahmed, Madiha Bashir https://creativecommons.org/licenses/by-nc-sa/4.0
Entrepreneurship in the Era of Artificial Intelligence: Redefining Opportunity Recognition, Labour Substitution, and Innovation Processes
This study examined the transformative role of artificial intelligence (AI) in entrepreneurship, focusing on opportunity recognition, labour substitution, and innovation processes. Rapid advancements in AI technologies altered traditional entrepreneurial practices by enabling data-driven decision-making, predictive analytics, and automation of routine tasks. Using a quantitative research design, data were collected from 350 entrepreneurs and managers of small and medium-sized enterprises (SMEs) who had adopted AI in their business operations. Descriptive statistics, reliability tests, regression analysis, and structural equation modelling (SEM) were employed to analyse the relationships between AI adoption and entrepreneurial outcomes. The results revealed that AI adoption significantly enhanced opportunity recognition by enabling entrepreneurs to identify emerging market trends, assess risks, and make informed strategic decisions. AI also facilitated labour substitution by automating repetitive tasks, allowing human resources to focus on creative and analytical roles. Moreover, AI-driven innovation processes accelerated product development, improved operational efficiency, and supported experimentation, thereby strengthening entrepreneurial performance. Despite these positive outcomes, challenges such as workforce displacement, ethical concerns, and limited access to AI technologies were identified as barriers to full adoption. The study concluded that AI functions as a strategic enabler that reshapes entrepreneurial practices, labour dynamics, and innovation strategies. The findings provide valuable insights for entrepreneurs, policymakers, and academic institutions to implement adaptive strategies for sustainable and inclusive entrepreneurial growth in the era of artificial intelligence. References Ali, A., & Rafiq-uz-Zaman, M. (2025). Institutional inertia vs. ethical innovation: A comparative analysis of AI governance at The Islamia University of Bahawalpur and Cambridge University Press. Contemporary Journal of Social Science Review, 3(4), 91–102. https://doi.org/10.63878/cjssr.v3i4.1695 Asif, M., Shahid, S., & Rafiq-uz-Zaman, M. (2025). Immersive technologies, awe, and the evolution of retail in the metaverse. International Premier Journal of Languages & Literature, 3(4), 713–748. https://ipjll.com/ipjll/index.php/journal/article/view/295 Audretsch, D. B., & Belitski, M. (2023). Artificial intelligence, entrepreneurship, and economic growth. Small Business Economics, 61(3), 1017–1035. https://doi.org/10.1007/s11187-022-00673-4 Badghish, M., & Soomro, T. R. (2024). Artificial intelligence and entrepreneurial innovation: Evidence from emerging markets. Technological Forecasting and Social Change, 191, 122450. https://doi.org/10.1016/j.techfore.2023.122450 Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. https://doi.org/10.1257/mac.20180345 Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2018). Artificial intelligence: The next digital frontier? McKinsey Global Institute. https://doi.org/10.2139/ssrn.3213930 Bui, T. H., & Duong, P. T. (2024). AI-enabled opportunity recognition: A study on startup decision-making. Journal of Business Research, 161, 113556. https://doi.org/10.1016/j.jbusres.2023.113556 Cockburn, I. M., Henderson, R., & Stern, S. (2019). The impact of artificial intelligence on innovation. NBER Working Paper, 24449. https://doi.org/10.3386/w24449 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., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 Fossen, F. M., McLemore, T., & Sorgner, A. (2024). Artificial intelligence and entrepreneurship. Foundations and Trends® in Entrepreneurship, 20(8), 781–904. https://doi.org/10.1561/0300000130 Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392 Jamil, R., Zhang, Y., Anwar, M., & Mustafa, S. (2025). Inclusive artificial intelligence in entrepreneurship: Access, adoption, and equity implications. Small Business Economics, 65(2), 1105–1124. https://doi.org/10.1007/s11187-024-00850-7 Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2022). Digital transformation: An overview of the current state of the art of research. Journal of Small Business Management, 60(1), 1–25. https://doi.org/10.1080/00472778.2020.1766690 Li, X., Wang, J., & Liu, X. (2022). Artificial intelligence capability and entrepreneurial opportunity recognition. Journal of Business Venturing Insights, 18, e00313. https://doi.org/10.1016/j.jbvi.2022.e00313 Machucho, J., & Ortiz, P. (2025). The role of artificial intelligence in entrepreneurial opportunity identification. Journal of Small Business Management, 63(1), 67–88. https://doi.org/10.1080/00472778.2023.2198765 Mariani, M. M., Perez-Vega, R., & Wirtz, J. (2023). Artificial intelligence and innovation: A systematic review and research agenda. Technovation, 120, 102528. https://doi.org/10.1016/j.technovation.2022.102528 Mikalef, P., Fjørtoft, S. O., & Torvatn, H. Y. (2024). Artificial intelligence-enabled dynamic capabilities and firm performance. Journal of Business Research, 168, 114101. https://doi.org/10.1016/j.jbusres.2023.114101 Obschonka, M., Audretsch, D. B., & Volkmann, C. (2020). Artificial intelligence and big data in entrepreneurship. Small Business Economics, 55(2), 339–351. https://doi.org/10.1007/s11187-019-00186-0 Omidmand, P., Dorri, R., Mozaffari, A., & Ataei, S. (2025). Artificial intelligence applications in lean startup methodology: A bibliometric analysis of research trends and future directions. Journal of Small Business and Entrepreneurship Development, 13(1), 45-67. Park, J.-H., Kim, S.-J., & Lee, S.-T. (2025). AI and creativity in entrepreneurship education: A systematic review of LLM applications. AI, 6(5), 100. https://doi.org/10.3390/ai6050100 Rafiq-uz-Zaman, M. (2022). Strategic upskilling: Fusing technical expertise with human capabilities. Journal of Business Insight and Innovation, 1(1), 76–86. https://doi.org/10.5281/zenodo.17766381 Rafiq-uz-Zaman, M. (2023). Bridging CPEC-driven industrial growth and skill-based education in Pakistan: A systematic review. Journal of Business Insight and Innovation, 2(1), 55–78. https://insightfuljournals.com/index.php/JBII/article/view/57 Rafiq-uz-Zaman, M. (2023). Redesign for 21st-century skills: Preparing learners for a rapidly changing workforce. Journal of Business Insight and Innovation, 1(2), 89–102. https://insightfuljournals.com/index.php/JBII/article/view/58 Rafiq-uz-Zaman, M. (2024). Leveraging skill development and STEAM innovation for business growth: A strategic framework for enhancing workforce performance in emerging markets. Journal of Business Insight and Innovation, 3(1), 48–63. https://insightfuljournals.com/index.php/JBII/article/view/55 Rafiq-uz-Zaman, M. (2025). Beyond the blackboards: Building a micro-edtech economy through teacher-led innovation in low-income schools. Journal of Business Insight and Innovation, 4(1), 46–52. https://doi.org/10.5281/zenodo.16875721 Rafiq-uz-Zaman, M. (2025). From chalkboards to competence: Rethinking skill-based education in Pakistan for a business-led innovation economy. International Journal of Academic Research for Humanities, 5(3), 1–13. https://doi.org/10.5281/zenodo.17112058 Rafiq-uz-Zaman, M. (2025). Use of artificial intelligence in school management: A contemporary need of school education system in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984–2009. https://doi.org/10.62345/jads.2025.14.2.56 Rafiq-uz-Zaman, M. (2025). Between adoption and ambiguity: Navigating the AI policy vacuum in Pakistani higher education. Research Journal for Social Affairs, 3(6), 877–885. https://doi.org/10.71317/RJSA.003.06.0523 Rafiq-uz-Zaman, M., Malik, N., & Bano, S. (2025). Learning to innovate: WhatsApp groups as grassroots innovation ecosystems among micro-entrepreneurs in emerging markets. Journal of Asian Development Studies, 14(1), 1854–1862. https://doi.org/10.62345/jads.2025.14.1.47 Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 Review of Managerial Science. (2026). Artificial intelligence technologies and entrepreneurship: A hybrid literature review. Advance online publication. https://doi.org/10.1007/s11846-025-00839-4 Siddiqui, D., Mumtaz, U., & Ahmad, N. (2024). Artificial intelligence in entrepreneurship: A bibliometric analysis of the literature. Journal of Global Entrepreneurship Research, 14(1), 1–27. https://doi.org/10.1007/s40497-024-00385-5 Sirait, M., Hidayat, R., & Nugroho, Y. (2025). Artificial intelligence, labor substitution, and human capital development in startups. 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Copyright (c) 2026 Muhammad Sarfraz Latif, Mansoor Ahmed Soomro, Asif Ahmad, Muhammad Irfan Syed https://creativecommons.org/licenses/by-nc-sa/4.0
Geopolitics of Energy Transition: United States Assistance to Pakistan
This article provides a comprehensive examination of the United States' role in addressing Pakistan's pressing energy security challenges. As a nation heavily reliant on energy imports, Pakistan faces the critical task of securing substantial technological and economic assistance from major global powers to satisfy its domestic energy requirements. The United States, as an established global leader, not only possesses the advanced technological capabilities necessary to support Pakistan's legitimate energy needs but also holds a strategic position to influence the region's energy landscape. Simultaneously, China has emerged as a key player, demonstrating a robust and growing interest in providing energy assistance to Pakistan. This dual interest from two of the world's foremost powers creates a complex geopolitical environment, requiring Pakistan to perform a delicate diplomatic balancing act to maintain constructive relations with both Washington and Beijing without alienating either. Employing Power Transition Theory as its central theoretical framework, this paper offers a critical and nuanced evaluation of the rationale underpinning U.S.-Pakistan green energy collaboration, explicitly juxtaposing it with the nature and scope of Chinese involvement in Pakistan's energy sector. The study utilizes a qualitative methodology, incorporating a mixed-methods approach to analyse a diverse array of primary and secondary data sources, including policy documents, bilateral agreements, and scholarly discourse. The central argument posits that the U.S. approach to facilitating a green energy transition in Pakistan is predominantly motivated by its own strategic security interests within the South Asian region, rather than purely developmental or environmental concerns. The analysis further concludes that in the face of intensifying strategic competition from China, the United States must accelerate and substantiate its energy cooperation efforts to remain a relevant and influential partner. Conversely, the findings suggest that Pakistan would benefit most from a diversified and pragmatic strategy, leveraging multiple energy partnerships to maximize its technological, economic, and political gains while navigating the complexities of great-power rivalry. References Abdullah, F. B., Iqbal, R., Jawaid, M., Memon, I., Mughal, S., Memon, F. S., & Rizvi, S. S. A. (2021). Energy security index of Pakistan (ESIOP). 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Overland (Ed.), Handbook on the geopolitics of the energy transition (pp. 123–140). Edward Elgar Publishing. https://doi.org/10.4337/9781800370432.00015 Government of Pakistan. (2021). Nationally determined contributions: Update 2021. United Nations Framework Convention on Climate Change. https://unfccc.int/sites/default/files/NDC/2022-06/Pakistan%20Updated%20NDC%202021.pdf International Energy Agency. (2022). Will new PV manufacturing policies in the United States, India, and the European Union create global PV supply diversification? https://www.iea.org/reports/will-new-pv-manufacturing-policies-in-the-united-states-india-and-the-european-union-create-global-pv-supply-diversification International Energy Agency. (2024). Renewables 2024: Analysis and forecast to 2030. https://www.iea.org/reports/renewables-2024 International Renewable Energy Agency. (2024). Renewable energy statistics 2024. https://www.irena.org/Publications/2024/Jul/Renewable-energy-statistics-2024 Janjua, S., Hassan, I., Ali, M. U., Ibrahim, M. M., Zafar, A., & Kim, S. (2021). Addressing social inequality and improper water distribution in cities: A case study of Karachi, Pakistan. Land, 10(11), Article 1278. https://doi.org/10.3390/land10111278 Lemke, D. (1997). The continuation of history: Power transition theory and the end of the Cold War. Journal of Peace Research, 34(1), 23–36. https://doi.org/10.1177/0022343397034001003 Li, J., Hu, G., Pengfei, S., & Jingli, S. (2007). China wind power report 2007. China Environmental Science Press. https://gwec.net/wp-content/uploads/2012/08/wind-power-report.pdf McBride, J., & Chatzky, A. (2019, May 13). Is "Made in China 2025" a threat to global trade? Council on Foreign Relations. https://www.cfr.org/backgrounder/made-china-2025-threat-global-trade Ministry of Planning, Development & Special Initiatives. (2024). Energy projects under CPEC. China-Pakistan Economic Corridor (CPEC) Secretariat. https://cpec.gov.pk/energy Organski, A. F. K. (1958). World politics. Knopf. Our World in Data. (2024). Solar (photovoltaic) panel prices. https://ourworldindata.org/grapher/solar-pv-prices Pepe, J. M., Grinschgl, J., & Westphal, K. (2023). US-China rivalry and its impact on the energy transformation: Difficult cooperation fraught with dilemmas. In H. Overland (Ed.), Handbook on the geopolitics of the energy transition (pp. 200–218). Edward Elgar Publishing. https://doi.org/10.4337/9781800370432.00021 Pomeranz, K. (2021). The great divergence. Princeton University Press. https://doi.org/10.1515/9781400836010 Rafiq-uz-Zaman, M. (2023). Bridging CPEC-driven industrial growth and skill-based education in Pakistan: A systematic review. Journal of Business Insight and Innovation, 2(1), 55–78. https://insightfuljournals.com/index.php/JBII/article/view/57 Raza, M. A., Khatri, K. L., & Hussain, A. (2022). Transition from fossilized to defossilized energy system in Pakistan. Renewable Energy, 190, 19–29. https://doi.org/10.1016/j.renene.2022.03.059 RIGL (Ritar International Group Limited). (2024, October 21). The cost of a 2MW battery storage system. https://www.ritarpower.com/industry_information/The-cost-of-a-2MW-battery-storage-system_269.html Salik, M. A. N. (2023, April 21). Issue brief on "US-Pakistan Green Alliance: Advancing bilateral cooperation on sustainable development". Institute of Strategic Studies Islamabad. https://issi.org.pk/issue-brief-on-us-pakistan-green-alliance-advancing-bilateral-cooperation-on-sustainable-development/ Sattar, A., & Sattar, A. (2020). Pakistan's foreign policy 1947-2019: A concise history (5th ed.). Oxford University Press. Tammen, R. L., Kugler, J., & Lemke, D. (2017). Foundations of power transition theory. In Oxford research encyclopedia of politics. Oxford University Press. https://doi.org/10.1093/acrefore/9780190228637.013.296 U.S. Embassy & Consulates in Pakistan. (2024, January 19). Factsheet: Pak-US Green Alliance. https://pk.usembassy.gov/u-s-pakistan-green-alliance-framework-improves-climate-smart-agriculture-promotes-sustainable-economic-growth-aug-1-2024/ U.S. Mission Pakistan. (2024, August 1). U.S.-Pakistan 'Green Alliance' framework improves climate-smart agriculture, promotes sustainable economic growth. U.S. Embassy & Consulates in Pakistan. https://pk.usembassy.gov/u-s-pakistan-green-alliance-framework-improves-climate-smart-agriculture-promotes-sustainable-economic-growth-aug-1-2024/ United Nations. (2024). Country profile - Pakistan. https://www.un.org/esa/earthsummit/pakis-cp.htm United States Agency for International Development. (2024, September 26). U.S. foreign assistance to Pakistan 1947-2024 [Data set]. https://foreignassistance.gov/ United States Trade Representative. (2025). Pakistan. https://ustr.gov/countries-regions/south-central-asia/pakistan The White House. (2024, June 13). Fact sheet: Partnership for global infrastructure and investment at the G7 summit. https://www.whitehouse.gov/briefing-room/statements-releases/2024/06/13/fact-sheet-partnership-for-global-infrastructure-and-investment-at-the-g7-summit-2/ The World Bank. (2020). Global photovoltaic power potential by country. https://documents1.worldbank.org/curated/en/466331592817725242/pdf/Global-Photovoltaic-Power-Potential-by-Country.pdf World Economic Forum. (2024). Fostering effective energy transition 2024. https://www.weforum.org/publications/fostering-effective-energy-transition-2024/ Zafar, M. R., & Raja, M. Y. S. (2022). The challenge of energy transition and energy security needs of Pakistan in 21st century. Global Foreign Policies Review, 1(1), 11–23. https://doi.org/10.31703/gfpr.2022(v-i).02
Copyright (c) 2026 Shamaim Ali, Dr. Sameera Imran, Laila Rehman, Aliza Abrar, Sadia Manzoor https://creativecommons.org/licenses/by-nc-sa/4.0
Artificial Intelligence in FinTech and Its Implications for International Trade Efficiency
This study examined the role of Artificial Intelligence (AI) integration in Financial Technology (FinTech) and its implications for international trade efficiency. The research investigated how AI-driven mechanisms such as automated document processing, enhanced risk assessment, fraud detection, and compliance systems contributed to transaction cost reduction and overall trade performance. Using quantitative analysis, the study evaluated relationships among AI adoption, operational efficiency variables, and international trade efficiency. The findings revealed that AI integration significantly improved trade efficiency by accelerating cross-border payment processes, minimizing financial risks, and enhancing regulatory transparency. Transaction cost reduction emerged as a critical mediating factor linking AI-enabled FinTech innovations to improved trade outcomes. The results indicated that predictive analytics and machine learning models strengthened credit evaluation and fraud monitoring, thereby reducing uncertainty and information asymmetry in global trade transactions. The study concluded that AI-driven FinTech solutions functioned as strategic enablers of competitiveness in international markets by enhancing speed, reliability, and cost-effectiveness of trade finance operations. The findings provided empirical support for digital transformation theories within financial inter-mediation and highlighted the importance of supportive regulatory frameworks and digital infrastructure development. The study offered practical recommendations for policymakers and financial institutions seeking to leverage AI technologies to improve global trade efficiency. References Ali, A., & Rafiq-uz-Zaman, M. (2025). Institutional inertia vs. ethical innovation: A comparative analysis of AI governance at The Islamia University of Bahawalpur and Cambridge University Press. Contemporary Journal of Social Science Review, 3(4), 91–102. https://doi.org/10.63878/cjssr.v3i4.1695 Almoghayer, W. J. K., & Mahmoud, H. A. (2025). The adoption of cross-border payment systems: A comparative study of Belt and Road Initiative countries. Borsa Istanbul Review, 5(10), 011. https://doi.org/10.1016/j.bir.2025.10.011 (ScienceDirect) Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 37(3), 371–413. https://doi.org/10.2139/ssrn.2847806 Babina, T., Fedyk, A., He, A., & Hodson, J. (2023). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 147(2), 480–506. https://doi.org/10.1016/j.jfineco.2022.05.009 Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in finance: A comprehensive review through bibliometric and content analysis. SN Business & Economics, 4, 23. https://doi.org/10.1007/s43546-023-00618-x (Springer) Chen, M. A., Wu, Q., & Yang, B. (2019). How valuable is FinTech innovation? Review of Financial Studies, 32(5), 2062–2106. https://doi.org/10.1093/rfs/hhy130 Cui, J. (2025). The impact of AI technology on cross-border trade in Southeast Asia: A meta-analytic approach. arXiv. https://doi.org/10.48550/arXiv.2503.13529 (arXiv) Frost, J., Gambacorta, L., Huang, Y., Shin, H. S., & Zbinden, P. (2019). BigTech and the changing structure of financial intermediation. Economic Policy, 34(100), 761–799. https://doi.org/10.1093/epolic/eiaa003 Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. Journal of Finance, 77(1), 5–47. https://doi.org/10.1111/jofi.13090 Goldstein, I., Jiang, W., & Karolyi, G. A. (2019). To FinTech and beyond. Review of Financial Studies, 32(5), 1647–1661. https://doi.org/10.1093/rfs/hhz025 Gomber, P., Koch, J.-A., & Siering, M. (2018). Digital finance and FinTech: Current research and future research directions. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x Haddad, C., & Hornuf, L. (2019). The emergence of the global FinTech market. Small Business Economics, 53(1), 81–105. https://doi.org/10.1007/s11187-018-9991-x Jagtiani, J., & Lemieux, C. (2018). Do FinTech lenders penetrate areas that are underserved by traditional banks? Journal of Economics and Business, 100, 43–54. https://doi.org/10.1016/j.jeconbus.2018.03.001 Khalil, M. A., Padmanabhan, R., Hadid, M., Elomri, A., & Kerbache, L. (2025). AI-driven transformation in trade finance: A roadmap for automating letter of credit document examination. Digital Business, 5(2), 100130. https://doi.org/10.1016/j.digbus.2025.100130 (ScienceDirect) Lee, I., & Shin, Y. J. (2018). FinTech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46. https://doi.org/10.1016/j.bushor.2017.09.003 Lu, S., Liang, S., Xue, Q., & Bian, H. (2024). Enhancing cross-border payments: The convergence of AI and blockchain for currency exchange optimization. Applied and Computational Engineering, 75, 160–165. https://doi.org/10.54254/2755-2721/75/20240531 (EWA Direct) Najem, R., Bahnasse, A., Amr, M. F., et al. (2025). Advanced AI and big data techniques in E-finance: A comprehensive survey. Discover Artificial Intelligence, 5, 102. https://doi.org/10.1007/s44163-025-00365-y (Springer) Ozturk, O. (2024). The impact of AI on international trade: Opportunities and challenges. Economics, 12(11), 298. https://doi.org/10.3390/economy12110298 (MDPI) Philippon, T. (2016). The FinTech opportunity. Journal of Economic Perspectives, 30(2), 179–200. https://doi.org/10.1257/jep.30.2.179 Rafiq-uz-Zaman, M. (2022). Redesign for 21st-Century Skills: Preparing Learners for a Rapidly Changing Workforce. Journal of Business Insight and Innovation, 1(2), 89–102. Retrieved from https://insightfuljournals.com/index.php/JBII/article/view/58 Rafiq-uz-Zaman, M. (2025). Between Adoption and Ambiguity: Navigating the AI Policy Vacuum in Pakistani Higher Education. Research Journal for Social Affairs, 3(6), 877-885. https://doi.org/10.71317/RJSA.003.06.0523 Rafiq-uz-Zaman, M. (2025). Use of Artificial Intelligence in School Management: A Contemporary Need of School Education System in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984-2009. https://doi.org/10.62345/jads.2025.14.2.56 Ruqnuzzaman, M. (2025). Explainable AI and blockchain for dual-currency payments. Research Square. https://doi.org/10.21203/rs.8004820/v1 (Research Square) Saqib, H. M., & Amin, H. (2026). Comparative analysis of AI regulation for FinTech cybersecurity and privacy in the EU and Qatar. Discover Artificial Intelligence, 6, 59. https://doi.org/10.1007/s44163-025-00736-5 (Springer) Thakor, A. V. (2020). FinTech and banking: What do we know? Journal of Financial Intermediation, 41, 100833. https://doi.org/10.1016/j.jfi.2019.100833 Upputuri, V. (2025). The future of international transactions: How AI is transforming cross-border payments. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1515–1523. https://doi.org/10.32628/CSEIT251112157 (IJ of Scientific Research in CS) Vives, X. (2019). Digital disruption in banking. Annual Review of Financial Economics, 11, 243–272. https://doi.org/10.1146/annurev-financial-100719-120854 Zetzsche, D. A., Buckley, R. P., Arner, D. W., & Barberis, J. N. (2017). RegTech: The next generation of financial regulation. Georgetown Journal of International Law, 48, 593–678. https://doi.org/10.2139/ssrn.3047803
Copyright (c) 2026 Ali Raza, Muhammad Ali https://creativecommons.org/licenses/by-nc-sa/4.0
When Brands Listen Back: Adaptive Marketing Systems, Consumer Feedback Loops, and the Emergence of Responsive Market Intelligence
The rapid evolution of digital technologies has transformed traditional marketing systems into adaptive, feedback-driven architectures capable of generating real-time strategic intelligence. This study investigated how consumer feedback loops and AI-driven predictive analytics contributed to the emergence of responsive market intelligence in digitally intensive organizations. Drawing upon dynamic capabilities and market orientation perspectives, the research employed a quantitative cross-sectional design using survey data collected from marketing and analytics professionals. The findings revealed that structured consumer feedback mechanisms significantly enhanced real-time insight generation and strategic learning processes. AI and predictive analytics integration strengthened forecasting accuracy and personalization effectiveness, while organizational responsiveness emerged as the strongest predictor of responsive market intelligence. Mediation analysis further indicated that technological capabilities generated optimal value when supported by agile decision-making structures. The results demonstrated that adaptive marketing systems functioned as comprehensive strategic capabilities rather than isolated technological tools. By aligning feedback infrastructures with predictive analytics and agile organizational processes, firms improved strategic agility, customer engagement, and competitive positioning. The study contributed to contemporary marketing scholarship by integrating feedback loop theory with AI-enabled analytics to conceptualize responsive market intelligence as a dynamic, learning-oriented capability. Managerial implications emphasized system integration, cross-functional coordination, and ethical data governance as critical success factors for sustainable adaptive marketing transformation in volatile digital markets. References Ahmed, N., & Zhou, L. (2024). Advancing marketing measurement through AI-integrated continuous feedback loops. Research Square. Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018 Aslam, M., & Asif, M. (2025). Organizational power structures and the reproduction of gender inequality. Apex Journal of Social Sciences, 4(1), 57–67. Balamurugan, M. (2024). AI-driven adaptive content marketing: Automating strategy adjustments for enhanced consumer engagement. International Journal for Multidisciplinary Research, 6(5), Article 27940. https://doi.org/10.36948/ijfmr.2024.v06i05.27940 Bansal, R., Murthy, Y. S., Pruthi, N., Aziz, A. L., & Propheto, A. (2025). Consumer intensity drivers and adaptive marketing agility: Empirical evidence of mediating spillovers and co-evolution with moderating algorithmic amplification. Journal of Innovation and Technology in Marketing, 10, Article 100701. https://doi.org/10.1016/j.joitmc.2025.100701 Beyari, H. (2025). The role of artificial intelligence in personalizing social media marketing strategies for enhanced customer experience. Applied College Journal. Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance. MIS Quarterly, 24(1), 169–196. https://doi.org/10.2307/3250983 Day, G. S. (2011). Closing the marketing capabilities gap. Journal of Marketing, 75(4), 183–195. https://doi.org/10.1509/jmkg.75.4.183 Gooljar, V., Issa, T., Hardin-Ramanan, S., & Abu-Salih, B. (2024). Sentiment-based predictive models for online purchases in the era of Marketing 5.0: A systematic review. Journal of Big Data, 11, Article 107. https://doi.org/10.1186/s40537-024-00947-0 Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9 Kane, G. C., Palmer, D., Phillips, A. N., & Kiron, D. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review, 14(1). https://doi.org/10.2139/ssrn.2674865 Kasemrat, R., Kraiwanit, T., & Yuenyong, N. (2025). Predictive analytics in customer behavior: Unveiling insights through machine learning. Journal of Governance & Regulation, 14(1). https://doi.org/10.22495/jgrv14i1siart8 Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1–18. https://doi.org/10.2307/1251866 Kumar, V. (2024). AI-powered marketing: What, where, and how? Journal of Marketing Analytics Review. https://doi.org/10.1016/S0268-4012(24)00031-8 Ledro, C., Nosella, A., Vinelli, A., & Souverain, T. (2025). Artificial intelligence in customer relationship management: A systematic framework for successful integration. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2025.115531 Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45, 135–155. https://doi.org/10.1007/s11747-016-0495-4 Mikalef, P., Krogstie, J., Pappas, I., & Pavlou, P. (2019). Investigating the effects of big data analytics capabilities on firm performance. Information & Management, 56(8), Article 103207. https://doi.org/10.1016/j.im.2019.103207 Mohiuddin, D. (2026). Adaptive marketing systems and consumer feedback loops: Implications for market development in emerging economies. Journal of Business Insight and Innovation, 5(1), 37–48. Paul, R., Imam, M. H., & Jahan Mou, A. (2023). AI-powered sentiment analysis in digital marketing: A review of customer feedback loops in IT services. American Journal of Scholarly Research and Innovation, 2(2), 166–192. https://doi.org/10.63125/61pqqq54 Preprints.org. (2025). Organizational and technological barriers to AI-driven marketing strategies in FMCG. https://doi.org/10.20944/preprints202506.2425.v1 Roberts, N., & Grover, V. (2012). Investigating firm’s customer agility and firm performance. Journal of Management Information Systems, 28(4), 231–270. https://doi.org/10.2753/MIS0742-1222280409 Romano, G. (2024). The role of customer feedback loops in driving continuous growth and innovation. NJQIBE Journal. Teece, D. J. (2007). Explicating dynamic capabilities. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640 Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science, 36(1), 1–10. https://doi.org/10.1007/s11747-007-0069-6 Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13(3), 247–252. https://doi.org/10.1177/1094670510375461 Wamba, S. F., Gunasekaran, A., Akter, S., Dubey, R., Ngai, E. W. T., & Papadopoulos, T. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Copyright (c) 2026 Shahid Hussain, Dr Sanya Shahid , Muhammad Ameer Hamza https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Based Adaptive Learning Systems and their role in Enhancing Student Academic Performance
The purpose of the study is to explore students’ perception regarding AI-based adaptive learning systems and examine how students perceive that AI systems have impacted their academic performance. The study is focused on such fundamental areas like awareness, perceived usefulness, personalization, engagement, challenges, and behavioural intention to use adaptive technology on learning. The research approach was quantitative and survey-based design. The data was collected using a sample of 300 students of different levels of education who were previously exposed to AI-based adaptive learning systems. On the basis of the demographic features and various dimensions of adaptive learning, the responses were gathered with the assistance of a structured questionnaire. The analysis of the data was based on descriptive statistical tools, including frequencies, percentages, mean scores, and standard deviations. The results prove that the students are highly aware of the AI-based adaptive learning systems and that the students have high perceived usefulness of the technologies. It was also discovered that individualization and flexibility were strengths particularly in providing of personalized learning pathways and pacing. The research also indicates the presence of a positive impact on student engagement, self-directed learning, and perceived academic performance, particularly in reaching the learning objectives and positively affecting test performance. Nevertheless, such threats like the limitation of the internet, technical difficulties, and usability problems were discovered to impede optimal use. In spite of these obstacles, students were also reported to have a high behavioural intention to use AI-based adaptive learning systems again. The paper concludes that AI-based adaptive learning systems can be highly effective to improve the student learning process and academic performance in case they are correctly implemented. To maximise their educational impact, it is necessary to address infrastructural and technical issues, as well as to offer institutional support and guidance. The results provide useful information to teachers, learning institutions, and system developers aiming to incorporate adaptive learning technologies to educational practice. References Adewale, M. D., Azeta, A., Abayomi-Alli, A., & Sambo-Magaji, A. (2024). Impact of artificial intelligence adoption on students' academic performance in open and distance learning: A systematic literature review. Heliyon, 10(22), Article e39579. https://doi.org/10.1016/j.heliyon.2024.e39579 Afshar, M. Z., & Shah, M. H. (2025). Leveraging Porter's diamond model: Public sector insights. The Critical Review of Social Sciences Studies, 3(2), 2255–2271. Afzal, M., Arshad, N., & Shaheen, A. (2025). ChatGPT and the future of academic writing: Enhancing productivity and creativity. Journal of Engineering and Computational Intelligence Review, 3(1), 1–11. Ahmed, S., & Asif, M. (2026). Public opinion on the effectiveness of local government anti-corruption measures: A multi-city survey analysis. International Journal of Social Sciences Bulletin, 4(1), 1189–1201. https://doi.org/10.5281/zenodo.18412790 Arshad, N., Baber, M. U., & Ullah, A. (2024). Assessing the transformative influence of ChatGPT on research practices among scholars in Pakistan. Mesopotamian Journal of Big Data. Advance online publication. Asif, M., & Asghar, R. J. (2025). Managerial accounting as a driver of financial performance and sustainability in small and medium enterprises in Pakistan. Center for Management Science Research, 3(7), 150–163. https://doi.org/10.5281/zenodo.17596478 Asif, M., Ali, A., & Shaheen, F. A. (2025). Assessing the Effects of Artificial Intelligence in Revolutionizing Human Resource Management: A Systematic Review. Social Science Review Archives, 3(4), 2887–2908. https://doi.org/10.70670/sra.v3i3.1055 Cho, Y. C. (2022). Effects of AI-based personalized adaptive learning system in higher education. Journal of the Korean Association of Information Education, 26(4), 249–263. https://doi.org/10.14352/jkaie.2022.26.4.249 Das, S., Mutsuddi, I., & Ray, N. (2025). Artificial intelligence in adaptive education: A transformative approach. In Advancing adaptive education: Technological innovations for disability support (pp. 21–50). IGI Global. Ezzaim, A., Dahbi, A., Assad, N., & Haidine, A. (2022). AI-based adaptive learning: State of the art. Proceedings of the International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 155–167). Springer. https://doi.org/10.1007/978-3-031-09342-0_13 Gupta, T., Kumar, A., Roy, B. K., & Saini, S. (2024). Adaptive learning systems: Harnessing AI to personalize educational outcomes. International Journal for Research in Applied Science and Engineering Technology, 12(11), 458–464. Habib, H., Jelani, S. A. K., & Najla, S. (2022). Revolutionizing inclusion: AI in adaptive learning for students with disabilities. Multidisciplinary Science Journal, 1(1), 1–11. https://doi.org/10.36948/msj.2022.v01i01.378 Hasan, M. A., Mazumder, M. T. R., Motari, M. C., Shourov, M. S. H., & Sarkar, M. (2025). AI-powered fraud detection: Strengthening risk monitoring with business intelligence in US financial institutions. Journal of International Accounting and Financial Management, 2(2), 162–176. Imtiaz, N., Zannat, F., Ahmed, S., Hasan, M. A., & Mahmud, S. (2025). Leveraging AI for data-driven decision making and automation in the USA education sector. Journal of Economics, Management & Business Administration, 4(1), 87–106. Iqbal, M. H. (2023). A review on the role of IoT in modern electrical engineering education. Journal of Engineering and Computational Intelligence Review, 1(1), 14–22. Joshi, M. A. (2024). Adaptive learning through artificial intelligence. International Journal on Integrated Education, 7(2), 41–43. Mustafa, G., Urooj, T., & Aslam, M. (2024). Role of artificial intelligence for adaptive learning environments in higher education by 2030. Journal of Social Research Development, 5(3), 1–15. Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), Article 596. https://doi.org/10.3390/info15100596 Saleem, S., Aziz, M. U., Iqbal, M. J., & Abbas, S. (2025). AI in education: Personalized learning systems and their impact on student performance and engagement. The Critical Review of Social Sciences Studies, 3(1), 2445–2459. Sari, H. E., Tumanggor, B., & Efron, D. (2024). Improving educational outcomes through adaptive learning systems using AI. International Transactions on Artificial Intelligence, 3(1), 21–31. https://doi.org/10.54216/ITAI.030102 Ullah, A., Shahzad, F., Ur Rehman, A., Naseer, M., & Akhtar, N. (2024). Analyzing the students' attitudes and behavior towards traditional classes and technology-enhanced online learning. International Journal of Social Science Archives, 7(3), 45–60. Wang, X., Huang, R. T., Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., & Martin, F. (2024). The efficacy of artificial intelligence-enabled adaptive learning systems from 2010 to 2022 on learner outcomes: A meta-analysis. Journal of Educational Computing Research, 62(6), 1348–1383. https://doi.org/10.1177/07356331241234567 Younus, A. M., Abumandil, M. S., Gangwar, V. P., & Gupta, S. K. (2022). AI-based smart education system for a smart city using an improved self-adaptive leap-frogging algorithm. In AI-centric smart city ecosystems (pp. 231–245). CRC Press. Zaman, M. A. U., & Akhter, E. (2023). Adaptive learning systems for English literature classrooms: A review of AI-integrated education platforms. International Journal of Scientific Interdisciplinary Research, 4(3), 56–86.
Copyright (c) 2026 Mahrukh Rehman Mirza, Sadaf Raja, Amal Noor Nizami https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Supply Chain Ecosystems: Artificial Intelligence Enabled Integration of Planning, Execution, and Performance Management
The rapid evolution of digital technologies has transformed traditional supply chain models into intelligent, interconnected ecosystems. This study investigated the role of Artificial Intelligence (AI) in enabling the integration of planning, execution, and performance management within smart supply chain ecosystems. A quantitative research design was employed to collect data from supply chain professionals across manufacturing and service sectors. Statistical analyses, including reliability testing, correlation, regression, and mediation analysis, were conducted to evaluate the relationships among AI-enabled planning, AI-enabled execution, AI-enabled performance management, and supply chain performance. The findings revealed that AI integration significantly improved forecasting accuracy, operational efficiency, responsiveness, and overall performance. AI-enabled execution emerged as the strongest direct predictor of supply chain performance, while AI-enabled performance management played a mediating role in strengthening the linkage between strategic planning and operational outcomes. The results emphasized that holistic AI integration across supply chain functions yielded greater performance benefits than isolated technological implementations. The study contributed to the theoretical advancement of smart supply chain ecosystem frameworks and provided practical insights for organizations seeking sustainable competitive advantage in volatile environments. The findings underscored the importance of ecosystem-level integration, governance mechanisms, and workforce readiness in maximizing AI-driven transformation. References Ali, A. A. A., Sharabati, A.-A. A., Alqurashi, D. R., & Shkeer, A. S. (2024). Impact of AI and supply chain collaboration on resilience. Uncertain Supply Chain Management, 12, 1801–1818. https://doi.org/10.5267/j.uscm.2024.1.004 Anumula, S. K., Krishnapillai, V., & Rai, D. K. (2025). Optimizing supply chain management with AI-powered predictive analytics. Journal of International Commercial Law and Technology, 6(1), 244–252. https://doi.org/10.61336/jiclt.25-01-20 Asif, M., Shah, H., & Asim, H. A. H. (2025). Cybersecurity and audit resilience in digital finance: Global insights and the Pakistani context. Journal of Asian Development Studies, 14(3), 560–573. https://doi.org/10.62345/jads.2025.14.3.47 Babai, M. Z., Arampatzis, M., Hasni, M., Lolli, F., & Tsadiras, A. (2024). On the use of machine learning in supply chain management: A systematic review. IMA Journal of Management Mathematics, 36(1), 21–49. https://doi.org/10.1093/imaman/dpae029 Bahroun, Z., Saihi, A., As'ad, R., & Tanash, M. (2025). A systematic analysis of generative artificial intelligence for supply chain transformation. Supply Chain Analytics, 13, Article 100188. https://doi.org/10.1016/j.sca.2025.100188 Bukhari, S. T., Rafiq-uz-Zaman, M., & Bano, S. (2025). Analysing the impact of education policies and their implementation on the school education system in Punjab, Pakistan. Inverge Journal of Social Sciences, 4(1), 98–110. https://doi.org/10.63544/ijss.v4i1.136 Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, Article 104132. https://doi.org/10.1016/j.compind.2024.104132 Daios, A. (2025). AI applications in supply chain management: A survey. Applied Sciences, 15(5), Article 2775. https://doi.org/10.3390/app15052775 Iseri, F., Iseri, H., Chrisandina, N. J., & colleagues. (2025). AI-based predictive analytics for enhancing data-driven supply chain optimization. Journal of Global Optimization. Advance online publication. https://doi.org/10.1007/s10898-025-01509-1 Jackson, I. (2024). Generative artificial intelligence in supply chain and operations management. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2024.2309309 Jones, J. (2025). Exploring the role of artificial intelligence in optimizing supply chain operations [Preprint]. Preprints.org. Jubair, H. (2025). The integration of artificial intelligence in supply chain management: A comprehensive review. ORGANIZE: Journal of Economics, Management and Finance, 4(1), 80–91. https://doi.org/10.58355/organize.v4i1.153 Kasih, E. W. K., Bernadi, B., & Yulianti, G. (2023). Exploring the impact of artificial intelligence on supply chain management performance: A scoping review. International Journal of Management, Accounting & Finance, 1(2), 188–XXX. https://doi.org/10.70142/kbijmaf.v1i2.188 Li, L., & colleagues. (2024). Generative AI-enabled supply chain management. International Journal of Production Economics, 267, Article 109XXX. Pan, Y., Wang, X., & Ye, Q. (2024). Enhancing supply chain management through artificial intelligence: A case study of JD Logistics. Advances in Economics, Management and Political Sciences, 109, 1–8. https://doi.org/10.54254/2754-1169/109/2024BJ0127 Rafiq-uz-Zaman, M. (2022). Comparative analysis of skill-based education in developed and developing countries. Inverge Journal of Social Sciences, 1(2), 90–95. https://doi.org/10.63544/ijss.v1i2.204 Rafiq-uz-Zaman, M. (2023). The impact of digital literacy on students' learning outcomes: A comprehensive review. Inverge Journal of Social Sciences, 2(2), 194–205. https://doi.org/10.63544/ijss.v2i2.210 Rafiq-uz-Zaman, M. (2025a). Beyond the blackboards: Building a micro-Edtech economy through teacher-led innovation in low-income schools. Journal of Business Insight and Innovation, 4(1), 46–52. https://doi.org/10.5281/zenodo.16875721 Rafiq-uz-Zaman, M. (2025b). The integrated skill-based education framework (ISEF): An empirically grounded model for reforming skill-based education in Pakistan. Global Social Sciences Review, X(III), 157–167. https://doi.org/10.31703/gssr.2025(X-III).14 Rafiq-uz-Zaman, M. (2025c). Use of artificial intelligence in school management: A contemporary need of school education system in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984–2009. https://doi.org/10.62345/jads.2025.14.2.56 Ricci, M. (2025). Intelligent supply chain management: Leveraging AI for visibility, resilience, and sustainable Industry 5.0 operations. International Journal of Advance Scientific Research, 5(08), 45–52. Samelius, A., & colleagues. (2025). Examining the integration of AI in supply chain management from Industry 4.0 to 6.0. Frontiers in Artificial Intelligence, 7, Article 1477044. https://doi.org/10.3389/frai.2024.1477044 Sheikh, A. A. (2025). AI-enabled digital technologies in supply chain management. RAMSS Journal. Taha, A., Khawaja, S., Qureshi, F., & Wahsheh, F. R. (2025). Employing artificial intelligence to improve the supply chain's resilience and performance: Moderating the impact of supply chain dynamics. Problems and Perspectives in Management, 23(1), 741–752. https://doi.org/10.21511/ppm.23.1.2025.55 Teixeira, A. R. (2025). A systematic literature review on AI applications in supply chain management. Information, 16(5), Article 399. https://doi.org/10.3390/info16050399 Wang, G. (2025). Application of artificial intelligence in supply chain management: Empirical analysis of optimization and efficiency enhancement. Informatica, 49(37), Article 10873. https://doi.org/10.31449/inf.v49i37.10873 Zaman, J., Shoomal, A., Jahanbakht, M., & Ozay, D. (2025). Driving supply chain transformation with IoT and AI integration. IoT, 6(2), Article 21. https://doi.org/10.3390/iot6020021 Zheng, G. (2025). Enhancing supply chain visibility with generative AI. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2025.2543964
Copyright (c) 2026 Zujaj Ahmed, Jauhar Abbas , Ahsan Basharat Hussain https://creativecommons.org/licenses/by-nc-sa/4.0
Impact of Green HRM on Employee Commitment: Moderating Role of Green Leadership
This study examined the impact of Green Human Resource Management (Green HRM) on employee commitment and investigated the moderating role of green leadership. A quantitative cross-sectional design was employed, and data were collected from 285 employees working in sustainability-oriented organizations. Hierarchical regression analysis was used to test the proposed hypotheses. The findings indicated that Green HRM had a significant positive effect on employee commitment (β = 0.64, p < 0.001) and explained 41% of the variance (R² = 0.41). When green leadership was added to the model, the explained variance increased to 52%, confirming its independent contribution. Furthermore, the interaction term between Green HRM and green leadership was significant (β = 0.21, p < 0.01), raising the total explained variance to 56%, which confirmed the presence of moderation. The results demonstrated that environmentally oriented HR practices enhanced employees’ emotional attachment and loyalty, particularly when supported by sustainability-driven leadership behaviours. The study contributed to sustainability and HRM literature by integrating structural HR mechanisms with leadership dynamics within a moderated regression framework. Practically, the findings suggested that organizations seeking to strengthen employee commitment should align green HR policies with authentic environmental leadership practices. The integration of sustainability-focused HR systems and green leadership created a reinforcing mechanism that supported long-term organizational commitment and environmental responsibility. References Afsar, B., Badir, Y., & Kiani, U. S. (2016). Linking spiritual leadership and employee pro-environmental behavior. Journal of Environmental Psychology, 45, 79–88. https://doi.org/10.1016/j.jenvp.2015.11.010 Ahmed, S., & Asif, M. (2026). 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Journal of Business Ethics, 116(1), 107–119. https://doi.org/10.1007/s10551-012-1452-x Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874–900. Dumont, J., Shen, J., & Deng, X. (2017). Effects of green HRM practices on employee workplace green behavior. Human Resource Management, 56(4), 613–627. https://doi.org/10.1002/hrm.21792 Edwards, J. R., & Cable, D. M. (2009). The value of value congruence. Journal of Applied Psychology, 94(3), 654–677. Fatima, N., Rafiq-uz-Zaman, M., Arshad, I., Rasheed, I., & Fatima, A. (2025). The role of human resource management in teacher training for inclusive education: A phenomenological study. Indus Journal of Social Sciences, 3(3), 551–564. https://doi.org/10.59075/ijss.v3i3.1921 Gomes, D. R., Ribeiro, N., Gomes, G., Ortega, E., & Semedo, A. (2024). Green HRM’s effect on employees’ eco-friendly behavior and green performance: A study in the Portuguese tourism sector. Sustainability, 16(22), 10005. https://doi.org/10.3390/su162210005 Gomes, J. F. S., et al. (2023). The moderating role of employees' biospheric value in the effects of GHRM on affective commitment and engagement. Sustainability, 15(3), 2190. https://doi.org/10.3390/su15032190 Graves, L. M., Sarkis, J., & Zhu, Q. (2013). How transformational leadership influences employee green behavior. Journal of Business Ethics, 122(2), 221–234. https://doi.org/10.1007/s10551-013-1768-2 Haider, S. A., et al. (2026). Green transformational leadership as a catalyst for sustainable environmental performance. Discover Sustainability, 7, 202. https://doi.org/10.1007/s43621-025-02497-8 Hatipoğlu, Z., & Akduman, G. (2025). The mediating role of sustainable leadership in green HRM practices and organizational commitment: A case study in Turkey. Sustainability, 17(11), 4991. https://doi.org/10.3390/su17114991 Jabbour, C. J. C., et al. (2020). Green HRM and environmental performance. Business Strategy and the Environment, 29(2), 777–788. https://doi.org/10.1002/bse.2396 Khan, K., et al. (2025). The impact of green HRM practices on green innovative work behaviour. BMC Psychology, 13, 96. https://doi.org/10.1186/s40359-025-02417-5 Khan, K., Shams, M. S., Khan, Q., Akbar, S., & Niazi, M. M. (2022). Relationship among green human resource management, green knowledge sharing, green commitment, and green behavior: A moderated mediation model. Frontiers in Psychology, 13, 924492. https://doi.org/10.3389/fpsyg.2022.924492 Kim, A., Kim, Y., Han, K., Jackson, S., & Ployhart, R. (2019). Multilevel influences of green HRM. Journal of Management, 45(3), 1231–1257. https://doi.org/10.1177/0149206318770730 Luu, T. T. (2018). Green HRM and environmentally specific servant leadership. Journal of Sustainable Tourism, 26(11), 1901–1922. https://doi.org/10.1080/09669582.2018.1526294 Malik, N., Rafiq-uz-Zaman, M., Bugti, M. A., & Bangulzai, W. A. (2025). Harassment of women in South Punjab politics: Impacts and strategies for enhanced leadership. ACADEMIA International Journal for Social Sciences, 4(3), 2575–2590. https://doi.org/10.63056/ACAD.004.03.0547 Meyer, J. P., Stanley, D. J., Herscovitch, L., & Topolnytsky, L. (2002). Affective, continuance, and normative commitment to the organization: A meta-analysis. Journal of Vocational Behavior, 61(1), 20–52. https://doi.org/10.1006/jvbe.2001.1842 Miah, M., Szabó-Szentgróti, G., & Walter, V. (2024). A systematic literature review on green human resource management (GHRM): An organizational sustainability perspective. Cogent Business & Management, 11(1), 2371983. https://doi.org/10.1080/23311975.2024.2371983 Nazir, O., Islam, J., & Rahman, Z. (2021). Effect of green HRM on environmental performance. Management of Environmental Quality, 32(1), 1–16. https://doi.org/10.1108/MEQ-02-2020-0022 Pham, N. T., Tučková, Z., & Jabbour, C. J. C. (2020). Greening the hospitality industry. 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Copyright (c) 2026 Nida Mohammad, Dr. Syed Shameel Ahmed Quadri, Abdul Basit, Muhammad Irfan Syed https://creativecommons.org/licenses/by-nc-sa/4.0