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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Modal Verb Variation in Pakistani and American English: A Corpus-based Comparative Study
This study explores the use of modal verbs in Pakistani and American English through a corpus-based analysis of data drawn from the Global Web-Based English (GloWbE) corpus. The study examines five modal verbs—must, should, may, might, and would—through a comparative analysis of their semantic, pragmatic, and collocational patterns in the two English varieties. Based on the frequency counts, concordance lines and collocational analysis, the study demonstrates that Pakistani English is more likely to be used in formal registers whereas American English is more conversational. There are also differences in collocational patterns that bear the cultural and practical impacts in both varieties. The findings provide an empirical evidence of regional variation in modal verb usage, contributing to the fields of World Englishes and Corpus Linguistics. Moreover, the study provides pedagogical implications in teaching English language based on the findings of the research, emphasizing the role of contextual and regional sensitivity in teaching and learning modal verbs. The study emphasizes the significance of corpus-based approaches in investigating authentic language use across different varieties of English. References Abdullayev, S. (2024). The pragmatic functions of modal verbs in discourse: A corpus-based study. Journal of New Century Innovations, 50(2), 148–150. Anwar, B. (2012). A sociolinguistic study of Urdu–English code-switching in Pakistan (Doctoral dissertation, Bahauddin Zakariya University, Multan). Anwar, B., & Maryam, S. (2017). The use of stative verbs as progressive in Pakistani English: A corpus-based study. Journal of Research in Social Sciences, 5(2), 121–134. Anwar, B., & Talaat, M. (2011). English in a non-native context: Distinctive features of Pakistani journalistic English. English Language and Literary Forum (ELF), 13(1), 25–42. Anwar, B., Ajmal, M., & Keezhatta, M. S. (2020b). Nativization of grammatical features in Pakistani English. Asian EFL Journal, 27(42), 92–106. Anwar, B., Butt, B., & Khan, R. M. I. (2019). Modal auxiliaries in Pakistani English newspapers: Social and pedagogical perspectives. Pakistan Social Sciences Review, 3(2), 164–177. Anwar, B., Iqbal, T., & Rasool, S. (2020a). Logical connectors in newspapers: A comparative study of Pakistani and British columns. Journal of Media Studies, 35(1). Anwar, N., & Butt, I. R. (2025). A comparative study of modality markers in Pakistani and US English: Evidence from GloWbE. International Journal of Social Sciences Bulletin, 3(11), 318–327. Arshad, N., Shahid, U., & Anwar, B. (2025). New Englishes and local context: Exploring strategies of pragmatic nativization in Pakistani English. Journal of Arts and Linguistics Studies, 3(3), 3565–3580. Biber, D. (2019). Text-linguistic approaches to register variation. Register Studies, 1(1), 42–75. Davies, M., & Fuchs, R. (2015). Expanding horizons in the study of World Englishes with the 1.9 billion word Global Web-based English Corpus (GloWbE). English World-Wide, 36(1), 1–28. D’Arcy, A., Haddican, B., Richards, H., Tagliamonte, S. A., & Taylor, A. (2013). Asymmetrical trajectories: The past and present of –body/–one. Language Variation and Change, 25(3), 287–310. Hundt, M. (2007). English mediopassive constructions: A cognitive, corpus-based study of their origin, spread, and current status (Vol. 58). Rodopi. Hunston, S., & Thompson, G. (Eds.). (2000). Evaluation in text: Authorial stance and the construction of discourse. Oxford University Press. Kachru, B. B. (2005). Asian Englishes: Beyond the canon (Vol. 1). Hong Kong University Press. Khan, F. W., Zafar, M. S., & Bukhtiar, H. (2021). A corpus analysis of modality in COVID-19–based editorials of Pakistani English newspapers. Balochistan Journal of Linguistics, 9. Leech, G. (2003). Modality on the move: The English modal. In R. Facchinetti, M. Krug, & F. Palmer (Eds.), Modality in contemporary English (pp. 223–240). Mahboob, A. (2009). English as an Islamic language: A case study of Pakistani English. World Englishes, 28(2), 175–189. McEnery, T., & Hardie, A. (2011). Corpus linguistics: Method, theory and practice (3rd ed.). Cambridge University Press. Mindt, D. (2025). Investigating diachronic shifts within a domain of English modality. English Language & Linguistics, 29(3), 505–526. Noshair, R., Arshad, M. U., & Hamza, M. A. (2025). Analyzing modal verbs as politeness markers in British and Pakistani English communication: A corpus-based comparative study. ASSAJ, 4(1), 3655–3666. Palmer, F. R. (2001). Mood and modality. Cambridge University Press. Rubab, Z., & Mahmood, M. A. (2025). Modal verbs in English and Shahmukhi Punjabi language: A corpus-driven comparative study. Journal of Applied Linguistics and TESOL, 8(1). Schneider, E. W. (2007). Postcolonial English: Varieties around the world. Cambridge University Press. Shahid, U., Anwar, B., & Amin, M. W. (2025). Cleft constructions in British and Pakistani English newspapers: A comparative analysis. Journal of Applied Linguistics and TESOL (JALT), 8(2), 2385–2395. Yasmeen, K., Nazeer, I., & Ahmad, K. (2022). Stance-modal and semi-modal verbs: A corpus-based study. Pakistan Social Sciences Review, 6(4), 30–40.
Copyright (c) 2026 Muhammad Dawood, Behzad Anwar, Maria Naureen https://creativecommons.org/licenses/by-nc-sa/4.0
Automation, Transparency, and Efficiency in Library Management through Blockchain and IoT Technologies
The purpose of this paper is to examine the literature on the applications of Blockchain and the Internet of Things (IoT) and to understand how libraries are engaging with this technology to enable users to feel more involved in the present and to share information about Library resources and services. Based on the existing literature, the fundamental models of Blockchain and its combination with IoT are extracted, documented, and illustrated, focusing on performance, implementation ability, and the layers discussed for both. The findings could also provide an empirical platform for comprehension of the technological and behavioural factors that drive adoption of blockchain and IoT to improve automation of services, transparency in records, and overall workplace efficiencies within libraries. The current study is a valuable addition to the body of library and information science literature in that it merges cutting-edge digital technologies with a widely accepted technology acceptance model, and its findings are of practical interest for librarians, system developers, and policy makers involved in smart library initiatives. The recent applications and current uses of blockchain and IoT in libraries are presented with the hope that they will help build up future work across various sectors. References Almarri, S., & Aljughaiman, A. (2024). Blockchain technology for IoT security and trust: A comprehensive systematic literature review. Sustainability, 16(23), 10177. https://doi.org/10.3390/su162310177 Al Sadawi, A., Hassan, M. S., & Ndiaye, M. (2021). A survey on the integration of blockchain with IoT to enhance performance and eliminate challenges. IEEE Access, 9, 54478–54497. https://doi.org/10.1109/ACCESS.2021.3070869 Asif, M., & Sandhu, M. S. (2023). Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance. Journal of Business Insight and Innovation, 2(2), 67–77. Bansal, A., Arora, D., & Suri, A. (2018). Internet of things: Beginning of new era for libraries. Library Philosophy and Practice, 1–7. Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications. Devi, P. D., Mirudhula, S., & Devi, A. (2021). Advanced library management system using IoT. In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 150–154). IEEE. https://doi.org/10.1109/ICICCS51141.2021.9432378 Ennajeh, L., & Najjar, T. (2024). Blockchain technology adoption through the UTAUT model: Exploring the mediating role of trust. Journal of Telecommunications and the Digital Economy, 12(1), 328–355. https://doi.org/10.18080/jtde.v12n1.873 Frederick, D. (2019). Blockchain for libraries: A practical guide. Library Technology Reports, 55(8), 1–35. Fruin, C., & Joshi, S. (2021). Blockchain in interlibrary loan systems: A feasibility study. Journal of Library Administration, 61(4), 456–470. https://doi.org/10.1080/01930826.2021.1905036 Haque, E. U., Shah, A., Iqbal, J., et al. (2024). A scalable blockchain-based framework for efficient IoT data management using lightweight consensus. Scientific Reports, 14, 58578. https://doi.org/10.1038/s41598-024-58578-7 Khan, A. U., Zhang, Z., Chohan, S. R., & Rafique, W. (2022). Factors fostering the success of IoT services in academic libraries: A study built to enhance library performance. Library Hi Tech, 40(6), 1976–1995. https://doi.org/10.1108/LHT-06-2021-0179 Khan, M. K., & Ullah, A. (2024). Implication of IoT and its impact on library services: An overview. Inverge Journal of Social Sciences, 3(2), 63–72. https://doi.org/10.63544/ijss.v3i2.94 Khan, M. K. (2025). Transforming libraries with blockchain technology: An overview of its potential implementation, benefits, and challenges. Inverge Journal of Social Sciences, 4(1), 75–86. https://doi.org/10.63544/ijss.v4i1.127 Kharche, A., Badholia, S., & Upadhyay, R. K. (2024). Implementation of blockchain technology in integrated IoT networks for constructing scalable ITS systems in India. Blockchain: Research and Applications, 5(2), 100188. https://doi.org/10.1016/j.bcra.2024.100188 Laboso, P., Aruldoss, M., Thiyagarajan, P., Lakshmi, T. M., & Wynn, M. (2026). Library systems and digital-rights management: Towards a blockchain-based solution for enhanced privacy and security. Information, 17(2), 137. https://doi.org/10.3390/info17020137 Lamm, K., & Levin, D. (2018). Blockchain for digital rights management in libraries. Library Hi Tech, 36(4), 567–580. https://doi.org/10.1108/LHT-01-2018-0004 Meth, K. (2020). Blockchain in libraries: A practical guide. Library Technology Reports, 56(5), 1–40. Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 Research on the construction scheme of smart library based on blockchain technology. (2024). Measurement: Sensors, 31, 100943. https://doi.org/10.1016/j.measen.2023.100943 Rouf, B. A., & Raja, S. (2023). Applications of the Internet of Things (IoT) in academic libraries. In Ingenious librarianship: Enriching self-reliance. Today and Tomorrow’s Printers and Publishers. Sakamoto, Y. (2019). Blockchain for library resource sharing. Journal of Library and Information Science, 43(2), 123–135. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Copyright (c) 2026 Muhammad Kabir Khan, Farha Zafira Binti Agos Lokman https://creativecommons.org/licenses/by-nc-sa/4.0
Physician-Patient Interaction Quality as a Predictor of Patient Satisfaction: Examining the Unique Contributions of Communication and Empathy in Pakistani Healthcare
To examine the associations among patients' perceptions of physician communication, physician empathy, and patient satisfaction in Pakistani hospital settings. A cross-sectional correlational study was conducted in public and private hospitals in Islamabad and Rawalpindi, Pakistan, between December 2024 and February 2025. Using purposive convenience sampling, 280 adult patients from psychiatry (n = 92), dermatology (n = 102), and cardiology (n = 86) completed the Communication Assessment Tool (CAT), the Jefferson Scale of Patient Perceptions of Physician Empathy (JSPPPE), and a 10-item patient satisfaction scale. Pearson correlations assessed bivariate associations. Multiple linear regression tested whether communication and empathy jointly predicted patient satisfaction. One-way ANOVA examined differences in satisfaction across specialties, and independent samples t-tests compared satisfaction by gender and hospital type. Physician communication was strongly correlated with physician empathy (r = .91, p < .001) and patient satisfaction (r = .90, p < .001). Physician empathy was also strongly correlated with patient satisfaction (r = .92, p < .001). In regression analysis, the model was significant, F(2, 277) = 825.47, p < .001, explaining 85.6% of the variance in patient satisfaction (R² = .856; adj. R² = .855). Both physician communication (B = 0.34, SE = 0.04, β = .39, p < .001) and physician empathy (B = 1.06, SE = 0.10, β = .56, p < .001) independently predicted higher patient satisfaction. No significant differences in satisfaction were found across specialties (F(2, 277) = 1.24, p = .29) or by gender (t(278) = 0.87, p = .38). Patients' perceptions of physician communication and empathy are strongly associated with patient satisfaction, and both contribute uniquely to satisfaction ratings. Interventions that strengthen patient-centred communication and empathic engagement may improve patient experience in time-constrained healthcare settings. References Aman-Ullah, A., Mehmood, W., Amin, S., & Abbas, Y. A. (2023). Corporate social responsibility and patient's intention to revisit: A serial mediation study witnessing the healthcare sector. Environmental Science and Pollution Research, 30(7), 17995–18023. https://doi.org/10.1007/s11356-022-23760-9 Arshad, M., Khan, S., Ahmad, S., Qamar, A., Naeem, H., & Arshad, A. (2026). Comparison of Physician Communication, Physician Empathy, Physician–Patient Concordance, and Patient Satisfaction Across Psychiatry, Cardiology, and Dermatology Departments. Inverge Journal of Social Sciences, 5(1), 268–278. https://doi.org/10.63544/ijss.v5i1.236 Atif, M., Sarwar, M. R., Azeem, M., Naz, M., Amir, S., & Nazir, K. (2016). Assessment of patient satisfaction with pharmacy services in Rawalpindi, Pakistan. International Journal of Clinical Pharmacy, 38(1), 145–151. https://doi.org/10.1007/s11096-015-0210-7 Derksen, F., Bensing, J., & Lagro-Janssen, A. (2013). Effectiveness of empathy in general practice: A systematic review. British Journal of General Practice, 63(606), e76–e84. https://doi.org/10.3399/bjgp13X660814 Doyle, C., Lennox, L., & Bell, D. (2013). A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open, 3(1), e001570. https://doi.org/10.1136/bmjopen-2012-001570 Hafeez, A., Kiani, A. G., Din, S. U., Muhammad, W., Butt, K., Shah, S. M. A., & Shaikh, B. T. (2004). Prescription and dispensing practices in public sector health facilities in Pakistan: Survey report. Journal of the Pakistan Medical Association, 54(4), 187–191. Hojat, M., Louis, D. Z., Maxwell, K., Markham, F., Wender, R., & Gonnella, J. S. (2011). Patient perceptions of physician empathy, satisfaction with physician, interpersonal trust, and compliance. International Journal of Medical Education, 2, 83–87. https://doi.org/10.5116/ijme.4d00.b701 Howick, J., Moscrop, A., Mebius, A., Fanshawe, T. R., Lewith, G., Bishop, F. L., Mistiaen, P., Roberts, N., Dieninytė, E., Hu, X.-Y., Aveyard, P., & Onakpoya, I. J. (2018). Effects of empathic and positive communication in healthcare consultations: A systematic review and meta-analysis. Journal of the Royal Society of Medicine, 111(7), 240–252. https://doi.org/10.1177/0141076818769477 Hussain, A., Asif, M., Jameel, A., Hwang, J., Sahito, N., & Kanwel, S. (2019). Promoting OPD patient satisfaction through different healthcare determinants: A study of public sector hospitals. International Journal of Environmental Research and Public Health, 16(19), 3719. https://doi.org/10.3390/ijerph16193719 Hussain, S., Khalid, N., Ikram, A., Mirza, H., Conroy, R. M., & Aslam, M. (2022). Doctors' communication skills in military hospitals—A critical analysis. Pakistan Armed Forces Medical Journal, 72(3), 1011–1015. https://doi.org/10.51253/pafmj.v72i3.6437 Irving, G., Neves, A. L., Dambha-Miller, H., Oishi, A., Tagashira, H., Verho, A., & Holden, J. (2017). International variations in primary care physician consultation time: A systematic review of 67 countries. BMJ Open, 7(10), e017902. https://doi.org/10.1136/bmjopen-2017-017902 Jalil, A., Zakar, R., Zakar, M. Z., & Fischer, F. (2017). Patient satisfaction with doctor–patient interactions: A mixed methods study among diabetes mellitus patients in Pakistan. BMC Health Services Research, 17(1), 155. https://doi.org/10.1186/s12913-017-2094-6 Kane, G. C., Gotto, J. L., Mangione, S., West, S., & Hojat, M. (2007). Jefferson Scale of Patient's Perceptions of Physician Empathy: Preliminary psychometric data. Croatian Medical Journal, 48(1), 81–86. Makoul, G., Krupat, E., & Chang, C. H. (2007). Measuring patient views of physician communication skills: Development and testing of the Communication Assessment Tool. Patient Education and Counseling, 67(3), 333–342. https://doi.org/10.1016/j.pec.2007.05.005 Mushtaq, M., Khan, M. A., Ullah, S., & Ahmad, W. (2024). Satisfaction of patients in government medical teaching institutes of Peshawar, Khyber Pakhtunkhwa, Pakistan: A cross-sectional analytical study. Journal of the Pakistan Medical Association, 74(5), 892–897. https://doi.org/10.47391/jpma.9990 Riess, H., Kelley, J. M., Bailey, R. W., Dunn, E. J., & Phillips, M. (2012). Empathy training for resident physicians: A randomized controlled trial of a neuroscience-informed curriculum. Journal of General Internal Medicine, 27(10), 1280–1286. https://doi.org/10.1007/s11606-012-2063-z Sardar, M. A., Hussain, S., & Malik, M. A. (2020). Patients' satisfaction with health care delivery in the outpatients department of the Combined Military Hospital, Rawalpindi. Pakistan Armed Forces Medical Journal, 70(6), 1816–1820. https://doi.org/10.51253/pafmj.v70i6.5850 Street, R. L., Jr., Makoul, G., Arora, N. K., & Epstein, R. M. (2009). How does communication heal? Pathways linking clinician–patient communication to health outcomes. Patient Education and Counseling, 74(3), 295–301. https://doi.org/10.1016/j.pec.2008.11.015
Copyright (c) 2026 Maham Arshad, Saira Khan, Suleman Ahmad, Amina Afzal, Muhammad Shoaib, Zohaib Hassan https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Driven Media Evolution: Redefining Journalism and Communication in the Digital Age
Artificial Intelligence (AI) has changed the world of journalism and digital communication at an extremely fast pace. To improve the efficiency of newsrooms, audience engagement, and data-driven production of content, media organizations are increasingly employing AI-driven technologies. Nevertheless, the topic of AI integration into journalism also provokes ethical issues associated with misinformation, algorithmic bias, and professionalism. This research intends to investigate the degree of awareness and application of Artificial Intelligence in media organizations, assess its influence on journalism activities and the quality of communication, address ethical issues related to the implementation of AI, and discuss the perspectives of AI-based technologies in journalism and communication. A survey design was adopted to conduct a quantitative study on a structured questionnaire, which relies on a five-point Likert scale. The survey was conducted among 267 respondents comprising of journalists, media practitioners, academicians and students who participated in media and communication industries. The descriptive statistical methods such as frequency, percentage, mean and standard deviation were used to analyse the data. The results indicate that there is a high awareness and positive attitudes towards AI in journalism. The respondents also found that AI enhances the efficiency of the newsroom and helps with data analysis (M = 4.31) and helps a journalist find trending topics in a short period (M = 4.27). The AI was also seen to improve the speed of news delivery (M = 4.33) and increase the level of engagement with the audience in the digital media setting. Nevertheless, the respondents raised concerns about possible risks that include misinformation (M = 4.30), algorithm bias, and lack of human creativity. Moreover, the respondents placed a strong emphasis on clear regulations and policies governing of AI in journalism (M = 4.35). The paper concludes that AI has emerged as a necessity in contemporary journalism, and it has greatly enhanced efficiency, data analysis, and interaction with the audience. Meanwhile, there is also a need to implement those responsibly and with ethical standards and professional training to make sure that AI technologies are being used in the media industry in a balanced and effective way. References Afshar, M. Z., & Shah, M. H. (2025). Examining the role of change management in enhancing organizational resilience in public sector entities. Center for Management Science Research, 3(3), 931–942. https://cmsrjournal.com/index.php/Journal/article/view/199 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. Ahmad, N., Haque, S., & Ibahrine, M. (2023). The news ecosystem in the age of AI: Evidence from the UAE. Journal of Broadcasting & Electronic Media, 67(3), 323–352. https://doi.org/10.1080/08838151.2023.2235229 Ali, W., & Hassoun, M. (2019). Artificial intelligence and automated journalism: Contemporary challenges and new opportunities. International Journal of Media, Journalism and Mass Communications, 5(1), 40–49. https://doi.org/10.20431/2454-9479.0501004 Alim, I., Akter, S., Afroz, Z., Al Prince, A., & Hasan, M. A. (2025). Business intelligence in the age of AI: Evaluating machine learning’s impact on U.S. economic productivity. Lead Sci Journal of Management, Innovation and Social Sciences, 1(3), 15–30. 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, 2024, 1–10. https://doi.org/10.58496/MJBD/2024/001 Benson, C. E., Okolo, C. H., & Oke, O. (2025). Exploring the impact of AI on traditional journalism: Conceptualizing the future of news reporting and media integrity in an AI-driven world. International Journal of Advanced Multidisciplinary Research and Studies, 5(3), 2523–2529. Butt, I. N., & Shah, S. (2025). The convergence of artificial intelligence and 6G networks: A transformative shift in computing and communications. Journal of Engineering and Computational Intelligence Review, 3(1), 68–81. Gondwe, G. (2023). Exploring the multifaceted nature of generative AI in journalism studies: A typology of scholarly definitions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4465446 Guzman, A. L., & Lewis, S. C. (2024). What generative AI means for the media industries, and why it matters to study the collective consequences for advertising, journalism, and public relations. Emerging Media, 2(3), 347–355. https://doi.org/10.1177/27523543231212182 Hafied, H., Irwanto, I., Surjatmodjo, D., & Latuheru, R. (2025). AI-driven media evolution: Exploring automated journalism’s impact on the industry’s future. Jurnal Kajian Jurnalisme, 8(2), 205–218. https://doi.org/10.24198/jkj.v8i2.52962 Hasan, M. A., Mazumder, M. T. R., Motari, M. C., Shourov, M. S. H., & Sarkar, M. (2026). AI and business intelligence integration for improved efficiency and reporting accuracy in small U.S. financial institutions. Journal of Fintech, Business, and Development, 3(1), 1–25. Jones, B., Jones, R., & Luger, E. (2022). AI ‘everywhere and nowhere’: Addressing the AI intelligibility problem in public service journalism. Digital Journalism, 10(10), 1731–1755. https://doi.org/10.1080/21670811.2022.2145328 Lewis, S. C., Guzman, A. L., Schmidt, T. R., & Lin, B. (2025). Generative AI and its disruptive challenge to journalism: An institutional analysis. Communication and Change, 1(1), Article 9. https://doi.org/10.1177/26314825241234567 Moran, R. E., & Shaikh, S. J. (2022). Robots in the news and newsrooms: Unpacking meta-journalistic discourse on the use of artificial intelligence in journalism. Digital Journalism, 10(10), 1756–1774. https://doi.org/10.1080/21670811.2021.1952098 Nasser El Erafy, A. (2023). Applications of artificial intelligence in the field of media. International Journal of Artificial Intelligence and Emerging Technology, 6(2), 19–41. Niazi, S. (2024). Big data analytics with machine learning: Challenges, innovations, and applications. Journal of Engineering and Computational Intelligence Review, 2(1), 38–48. Nwabueze, E. A., & Abiodun, G. (2025). The evolution of mass communication in the age of AI: Opportunities and challenges. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5219267 Paik, S. (2025). Journalism ethics for the algorithmic era. Digital Journalism, 13(4), 696–722. https://doi.org/10.1080/21670811.2024.2307785 Prakash, G., & Sabharwal, D. (2024). AI revolution in online media: Transforming content creation, distribution, and consumption. In Media and AI: Navigating the future (p. 179). Shi, Y., & Sun, L. (2024). How generative AI is transforming journalism: Development, application and ethics. Journalism and Media, 5(2), 582–594. https://doi.org/10.3390/journalmedia5020036 Simon, F. M. (2024). Escape me if you can: How AI reshapes news organisations’ dependency on platform companies. Digital Journalism, 12(2), 149–170. https://doi.org/10.1080/21670811.2022.2151209 Sundar, S. S., & Lee, E. J. (2022). Rethinking communication in the era of artificial intelligence. Human Communication Research, 48(3), 379–385. https://doi.org/10.1093/hcr/hqac009 Tong, J. (2018). Journalistic legitimacy revisited: Collapse or revival in the digital age? Digital Journalism, 6(2), 256–273. https://doi.org/10.1080/21670811.2017.1278475 Ullah, A., Islam, K., Ali, A., & Baber, M. (2024). Assessing the impact of social media addiction on reading patterns: A study of Riphah International University students. International Journal of Human and Society, 4(1), 1250–1262. Voinea, D. V. (2025). Reconceptualizing gatekeeping in the age of artificial intelligence: A theoretical exploration of artificial intelligence-driven news curation and automated journalism. Journalism and Media, 6(2), Article 68. https://doi.org/10.3390/journalmedia6020068 Wenger, D., Hossain, M. S., & Senseman, J. R. (2025). AI and the impact on journalism education. Journalism & Mass Communication Educator, 80(1), 97–114. https://doi.org/10.1177/10776958231234567
Copyright (c) 2026 Dr. Huma Tahir, Syed Imran Ali Shah, Hamza Zamir Kiani, Muhammad Zeeshan Zahid https://creativecommons.org/licenses/by-nc-sa/4.0
A Comprehensive Review of High-Impact Educational Research (2021–2025): Trends, Insights, and Emerging Practices
The literature review is a critical analysis of high-impact educational research published between 2021 and 2025, which identifies the most significant trends, evidence-based practices, and emerging innovations that influence the development of modern education. Pedagogical practices in the post-COVID period have changed to emphasize the effectiveness of student engagement, fair access, and the implementation of technology-based approaches to instruction. Personalized and adaptive learning with the help of digital transformation (artificial intelligence, interactive e-books, and immersive learning environments) has resulted in a measurable student performance and engagement improvement. Problem-Based Learning, Project-Based Learning, and formative assessment remain active learning approaches that support the development of critical thinking and problem-solving skills in a variety of educational settings. These advances notwithstanding, there are still issues such as gaps in teacher training, a lack of resources, and professional development. The new technologies hold the promise of innovation but pose threats to data privacy, algorithmic bias, and digital equity. These challenges cannot be met without systemic support, accountable AI implementation, and a commitment on the part of institutions to inclusive and student-centred learning. This review will summarize the existing literature and outline practical recommendations to be followed by policymakers, teachers, and scholars who want to maximize the learning process and overcome the changing digital environment. References Ajid, S. N., Kusumaningtyas, D., Ratih, K., & Lava, S. (2025). Strategies for Integrating Problem-Based Learning, Teaching Modules, and Formative Assessments to Enhance Learning Outcomes and Critical Thinking Skills. Indonesian Journal on Learning and Advanced Education (IJOLAE), 7(2), 340–357. https://doi.org/10.23917/ijolae.v7i2.8612 Albohamood, A. H., Alqattan, M. S., & Vizcarra, C. P. (2025). Real-time Student Engagement Monitoring in Classroom Environments using Machine Learning and Computer Vision. 2025 4th International Conference on Computing and Information Technology (ICCIT), 1–6. https://doi.org/10.1109/ICCIT63348.2025.10989406 Andayani, A., & Prabowo, S. (2025). HOTS-Based Interactive E-Books and Student Learning Outcomes: A Systematic Literature Review. International Journal of Education and Learning Studies, 1(3), 45–58. https://doi.org/10.64421/ijels.v1i3.23 Bashir, M., & Naseer, D. N. (2025). From Opportunity to Obstacle: Learners’ Experiences on the Use of Campus Management System. Inverge Journal of Social Sciences, 4(1), 148–161. https://doi.org/10.63544/ijss.v4i1.196 Bayani, F., Rokhmat, J., Hakim, A., & Sukarso, A. (2025). Research Trends in Analytical Thinking Skills for Science Education: Insights, Pedagogical Approaches, and Future Directions. International Journal of Ethnoscience and Technology in Education, 2(1), 52–68. https://doi.org/10.33394/ijete.v2i1.14142 Bensi, L. P., Bensi, M. E., Oliveros, A. G. G., & Torres, R. A. G. (2025). Student Preferences for Hybrid Versus Face-to-Face Learning Approaches: A Comparative Analysis of Engagement, Learning Outcomes, and Technological Challenges. International Journal of Research and Scientific Innovation, 12(3), 881–892. https://doi.org/10.51244/ijrsi.2025.12030074 Bizimana, E. (2025). Student Engagement in Learning: Exploring the Role of Perceived Student Cohesiveness, Equity, Teacher Support, and Teacher Autonomy Support Under the Framework of Self-Determination Theory. International Journal of Changes in Education. Advance online publication. https://doi.org/10.47852/bonviewijce52024500 Cena, J. F., Diez, M. B., Flores, M. R., Noquiao, H. P. P., & Quinco-Cadosales, M. N. (2025). Interactive Self-Learning Modules (ISLMs) in Sustaining Student Engagement and Learning Outcomes: A Meta-Synthesis. International Journal of Advanced Multidisciplinary Research and Studies, 5(4), 153-160. https://doi.org/10.62225/2583049x.2025.5.4.4782 Chow, W., Xu, B., Bhaumik, A., Peng, S., Guo, M., & Kurnia, S. (2025). Bridging Engagement and Learning Outcomes. Pacific Journal of Technology Enhanced Learning, 7(2), 29–30. https://doi.org/10.24135/pjtel.v7i2.224 Copland, S., Hills, M., & McKendrick-Calder, L. (2025). Structured Flexibility in Assessment: Students Perceptions of the Impact of Different Elements of Choice on Their Decision Making, Engagement, and Learning Experiences. Teaching and Learning Inquiry, 13, 1–18. https://doi.org/10.20343/teachlearninqu.13.61 Edmunds, T. K., & Little, R. (2025). Impacts of the Active Learning Classroom on Student Learning and Engagement: The Role of Technology. Canadian Journal for the Scholarship of Teaching and Learning, 16(1), Article 9. https://doi.org/10.5206/cjsotlrcacea.2025.1.16584 Firdaus, T., Mufidah, R., Hamida, R. N., Febrianti, R. I., & Guivara, A. E. R. (2025). Research trends and the impact of ChatGPT on educational environments. International Journal of Social Sciences and Education Research, 11(2), 112–128. https://doi.org/10.24289/ijsser.1609741 Fitratunisyah, F., Bactiar, I., & Jamaluddin, J. (2025). Research Trends of Problem Based Learning (PBL) Models to Improve Students' Critical Thinking Skills and Learning Outcomes (2016-2025): A Systematic Review. Current Educational Review, 1(4), 331–348. https://doi.org/10.56566/cer.v1i4.410 Hamdan, A., Elmunsyah, H., Maula, P. I., Sari, H. V., Abdullah, M. F., & Septianingsih, D. (2025). Systematic Evaluation of Adaptive Assessment in E-Learning: Contribution to Vocational Education. 2025 9th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 1–6. https://doi.org/10.1109/ICEEIE66203.2025.11254863 Huang, X., Gai, Y., Sun, L., Zhang, Y., Yang, D., Sheng, H., Huang, J.-M., & Li, Y. (2025). Research on Strategies for Enhancing Student Engagement and Retention in Engineering Education under a Technology-Enhanced Learning Environment. 2025 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), 1–6. https://doi.org/10.1109/TALE66047.2025.11346628 Irvani, A. I., Dewi, A. P., Gunawan, A., & Rahmaniar, A. (2025). Gamification and Student Engagement: Evidence, Challenges, and Future Directions. Eduscape: Journal of Education Insight, 3(3), 175–189. https://doi.org/10.61978/eduscape.v3i3.937 Jainuri, M., Kamid, K., Syaiful, S., & Huda, N. (2025). Microlearning Effectiveness in Higher Education: A Systematic Review and Meta-Analysis of Student Retention and Learning Outcomes. MATHEMA: JURNAL PENDIDIKAN MATEMATIKA, 7(2), 476–492. https://doi.org/10.33365/jm.v7i2.517 K, N., Kumar, B. H., Sonar, R., Lamba, A. K., Valiveti, V. S. M., & Kalra, G. (2025). Enhancing Student Engagement and Learning Outcomes in Higher Education through E-Learning and Restricted Boltzmann Machines. 2025 3rd International Conference on Data Science and Information System (ICDSIS), 1–5. https://doi.org/10.1109/ICDSIS65355.2025.11071086 Khan, M. (2025). Mentimeter Tool for Enhancing Student Engagement and Active Learning: A Literature Review. International Journal of Changes in Education. Advance online publication. https://doi.org/10.47852/bonviewijce52023801 Kristiyuana, K., Wuriningsih, Fr., Idammatussilmi, I., & Agung, A. (2025). Reforming Pedagogy in Indonesian Primary Schools: A Five-Year Systematic Review of Differentiated Instruction, Project-Based Learning, and Formative Assessment (2020-2025). Jurnal Ilmiah Profesi Pendidikan, 10(2), 1556–1569. https://doi.org/10.29303/jipp.v10i2.3413 Laid, S. M. T., & Adlaon, M. S. (2025). A Systematic Review of Innovative Teaching Strategies in Science: Exploring Hands-on Learning, Technology Integration, and Student-Centered Approaches. Acta Pedagogia Asiana, 4(2), 91–105. https://doi.org/10.53623/apga.v4i2.645 Le, U. M., Kassem, T., & Tran, T. (2025). Enhancing pharmaceutical calculation proficiency: Exploring self-directed learning and self-reinforcement assessment strategies for student success. Currents in Pharmacy Teaching and Learning, 17(6), Article 102323. https://doi.org/10.1016/j.cptl.2025.102323 Li, F., Liu, J., Wu, J., & Wu, Z. (2025). Personalized Learning in Graduate Education: Learning Analytics and Assessment Methods from an IT Industry Perspective. 2025 7th International Conference on Computer Science and Technologies in Education (CSTE), 201–206. https://doi.org/10.1109/CSTE64638.2025.11091918 Martín-Rodríguez, A., & Madrigal-Cerezo, R. (2025). Technology-Enhanced Pedagogy in Physical Education: Bridging Engagement, Learning, and Lifelong Activity. Education Sciences, 15(4), Article 409. https://doi.org/10.3390/educsci15040409 Maurer, M., & Aleem, S. (2025). Comparative Performance Analysis of Machine Learning Techniques for Engagement Assessment in Education. 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 128–133. https://doi.org/10.1109/ACDSA65407.2025.11166367 Nassiri, S. H., & Bunyamin, M. A. H. (2025). TRENDS AND RESEARCH FRONTIERS IN PHYSICS EDUCATION: A BIBLIOMETRIC ANALYSIS FROM YEAR 2021-2025. International Journal of Modern Education, 7(27), 626–642. https://doi.org/10.35631/ijmoe.727039 Ningsih, A. G. (2025). Exploring the Impact of Adaptive Real-Time Quiz Platforms with Differentiated Learning Features on Student Engagement and Learning Outcomes: A Mixed-Methods Approach. International Journal of Information and Education Technology, 15(6), 1165–1175. https://doi.org/10.18178/ijiet.2025.15.6.2329 Ojetunde, S., & Ramnarain, U. (2025). Like objects or like subjects? Effects of student-robot interaction (SRI) and mathematical ability on students learning outcomes. Smart Learning Environments, 12(1), Article 11. https://doi.org/10.1186/s40561-024-00345-2 Punnoose, N. J., Mathew, M. d., & Baxi, V. (2025). Enhancing Student Engagement Through a Curated Digital Learning Library Platform. 2025 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 1–5. https://doi.org/10.1109/WEEF-GEDC66748.2025.11256211 Razilu, Z., Sulfasyah, S., & Nappu, S. (2025). Mapping Global Research Trends in Interactive Learning Media: A Bibliometric Analysis of Scopus Publications (2021-2025). PPSDP International Journal of Education, 4(2), 411–424. https://doi.org/10.59175/pijed.v4i2.743 Ricky, A. S., Umamah, F., Amalia, S. N., & Trisnawati, E. (2025). SYSTEMATIC LITERATUR REVIEW (2021-2025): PENGEMBANGAN E-BOOK INTERAKTIF DALAM PEMBELAJARAN IPA DI SEKOLAH DASAR. EDUTECH: Jurnal Inovasi Pendidikan Berbantuan Teknologi, 5(3), 464–474. https://doi.org/10.51878/edutech.v5i3.6887 Rohman, A. Z., Hasaini, H. F., Setiawan, R., & Rumagit, R. Y. (2025). Predictive Framework for Adaptive Learning: Integrating Clickstream Analytics and Assessment Outcomes in LMS. 2025 7th International Conference on Cybernetics and Intelligent System (ICORIS), 235–240. https://doi.org/10.1109/ICORIS67789.2025.11296012 Santhoshkumar, S., Kalaimani, T. M., Chandra, H., Prasad, Parthasarathy, R., & Charaan, R. M. D. (2025). Machine Learning System for Comprehensive Student Assessment and Academic Performance Optimization. 2025 International Conference on Smart & Sustainable Technology (INCSST), 1–5. https://doi.org/10.1109/INCSST64791.2025.11210374 Song, J. Y. (2025). A Systematic Literature Review of Teacher Professional Development Based on Responsible AI Principles: Analysis of Domestic and International Research Trends (2020-2025). The Korea Association of Lifelong Education & Convergence (KALEC), 4(2), 37–55. https://doi.org/10.58207/kalec.2025.4.2.37 Stepanyan, K., Hasegawa, K., & Poon, H. (2025). What do I, as a student, think of my own work? Using summative self-assessment in a large technical module. Journal of Learning Development in Higher Education, (35). https://doi.org/10.47408/jldhe.vi35.1374 Studi, P., Matematika, P., Undikma, F., Zuhaida, N., Pujiastuti, E., & Zaenuri. (2025). Trends in Students Mathematical Problem-Solving Skills: A Systematic Review and Bibliometric Analysis of GeoGebra-Assisted Problem-Based Learning Research (2021-2025). Media Pendidikan Matematika, 13(2), 165–181. https://doi.org/10.33394/mpm.v13i2.18489 Suhaimi, S., Ghani, M. F. A., & Radzi, N. M. (2025). Does Student-centred Leadership Integrate with Collaborative, Distributive, and Instructional Leadership? A Recent Systematic Review (2021-2025). International Research-Based Education Journal, 7(2), 238–272. https://doi.org/10.17977/um043v7i22025p238-272 Syamsudin, F. I., Wiyono, K., Patriot, E. A., Ismet, I., Saparini, S., & Kamari, A. (2025). Trends in Technology Literacy Research (2021 to 2025): A Bibliometric Analysis with VOSviewer. Jurnal Materi Dan Pembelajaran Fisika, 15(2), 156–166. https://doi.org/10.20961/jmpf.v15i2.109121 Umri, N. A., Karnyoto, A., & Pardamean, B. (2025). The Impact of AI-Generated Hallucinations in Educational Settings: Trends, Gaps, and Future Directions. 2025 9th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 557–562. https://doi.org/10.1109/ICITISEE68184.2025.11355145 Utami, A., Rusmana, A. N., & Rahayu, D. (2025). CAPTURING CURRENT RESEARCH TRENDS ON THE UTILIZATION OF CHATGPT IN SCIENCE EDUCATION. Jurnal Pendidikan Matematika Dan IPA, 16(3), 564–581. https://doi.org/10.26418/jpmipa.v16i3.93046 Wali, M., Redhana, I. W., & Tika, I. N. (2025). TRENDS IN EDUCATIONAL GAMES TO IMPROVE STUDENTS' LEARNING MOTIVATION IN SCIENCE LEARNING: A SYSTEMATIC LITERATURE REVIEW. Jurnal Penelitian Pendidikan IPA, 11(11), 39–50. https://doi.org/10.29303/jppipa.v11i11.12121 Weng, Y., & Zhang, Y. (2025). Assessment of Personalized Learning in Immersive and Intelligent Virtual Classroom on Student Engagement. arXiv.org. https://doi.org/10.48550/arXiv.2501.07883 Xiao, F. (2025). Intelligent Deep Learning Framework for University Physical Education Teaching Quality Assessment. 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON), 1–5. https://doi.org/10.1109/SSITCON66133.2025.11342279 Yunus, M., & Bachtiar, B. (2025). Beyond Access: Reframing Engagement, Assessment, and Feedback in Open and Distance Learning at Universitas Terbuka. International Journal of Learning, Teaching and Educational Research, 24(6), 492–511. https://doi.org/10.26803/ijlter.24.6.26 Zhang, N. (2025). Enhanced English Classroom Teaching Quality Assessment Using Hybrid Deep Learning Architecture. 2025 Third International Conference on Networks, Multimedia and Information Technology (NMITCON), 1–6. https://doi.org/10.1109/NMITCON65824.2025.11187842
Copyright (c) 2026 Dr. Muhammad Rafiq-uz-Zaman, Yi-Huang Shih https://creativecommons.org/licenses/by-nc-sa/4.0
Green Finance Intelligence and Sustainable Capital Allocation: Artificial Intelligence Driven ESG Signal Processing and Capital Market Efficiency
The rapid expansion of sustainable investment markets has intensified the need for accurate Environmental, Social, and Governance (ESG) data analysis to support effective capital allocation. However, traditional analytical approaches often struggle to interpret the growing volume of heterogeneous sustainability information, leading to information asymmetry and suboptimal investment outcomes. This study examined the role of artificial intelligence-driven ESG signal processing in enhancing green finance intelligence and improving sustainable capital allocation and capital market efficiency. A quantitative research design was employed, utilizing primary data collected from 312 financial professionals working in banking, asset management, and financial technology sectors. Descriptive statistical analysis revealed strong agreement regarding the importance of AI adoption in sustainable finance, with artificial intelligence adoption recording the highest mean value (M = 4.14, SD = 0.69), followed by green finance intelligence (M = 4.09, SD = 0.68) and ESG signal processing capability (M = 4.07, SD = 0.72). Regression analysis demonstrated that artificial intelligence adoption significantly influenced green finance intelligence (β = 0.41, t = 7.86, p < 0.001), while ESG signal processing capability significantly improved sustainable capital allocation (β = 0.38, t = 6.94, p < 0.001). Additionally, green finance intelligence positively affected capital market efficiency (β = 0.35, t = 6.21, p < 0.001). The findings indicated that artificial intelligence significantly enhances ESG information processing, reduces information asymmetry, and supports data-driven sustainable investment strategies. The study concluded that AI-driven ESG analytics play a crucial role in strengthening sustainable finance ecosystems and improving the efficiency of capital allocation within modern financial markets. References Akash, V., & Kumari, R. (2025). The role of artificial intelligence in promoting sustainable and responsible investments. Journal of Social Review and Development, 4(S1), 182–186. https://doi.org/10.64171/JSRD.4.S1.182-186 Aruwaji, M. A., & Swanepoel, M. J. (2025). The impact of AI-integrated ESG reporting on firm valuation in emerging markets. Journal of Risk and Financial Management, 18(12), 675. https://doi.org/10.3390/jrfm18120675 Bae, K. H., El Ghoul, S., Gong, Z., & Guedhami, O. (2024). ESG performance and investment efficiency: The impact of information asymmetry. Journal of International Financial Markets, Institutions and Money, 91, 101919. https://doi.org/10.1016/j.intfin.2023.101919 Baker, M., Bergstresser, D., Serafeim, G., & Wurgler, J. (2021). Financing the response to climate change: The pricing and ownership of U.S. green bonds. Journal of Financial Economics, 142(2), 499–516. https://doi.org/10.1016/j.jfineco.2021.02.012 Berg, F., Koelbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26(6), 1315–1344. https://doi.org/10.1093/rof/rfac033 Bolton, P., & Kacperczyk, M. (2021). Do investors care about carbon risk? Journal of Financial Economics, 142(2), 517–549. https://doi.org/10.1016/j.jfineco.2021.05.008 Davidescu, A. A., Apostu, S., & Strat, V. A. (2025). Artificial intelligence in ESG and sustainable finance: A bibliometric analysis of research trends. Proceedings of the International Conference on Business Excellence, 19(1), 1506–1517. https://doi.org/10.2478/picbe-2025-0117 Elhady, A. M., & Shohieb, S. (2025). AI-driven sustainable finance: Computational tools, ESG metrics, and global implementation. Future Business Journal, 11, 209. https://doi.org/10.1186/s43093-025-00610-x Flammer, C. (2021). Corporate green bonds. Journal of Financial Economics, 142(2), 499–516. https://doi.org/10.1016/j.jfineco.2021.01.010 Friede, G., Busch, T., & Bassen, A. (2021). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 11(4), 210–233. https://doi.org/10.1080/20430795.2020.1718917 Gillan, S. L., Koch, A., & Starks, L. T. (2021). Firms and social responsibility: A review of ESG and CSR research in corporate finance. Journal of Corporate Finance, 66, 101889. https://doi.org/10.1016/j.jcorpfin.2021.101889 Giudici, P., & Wu, L. (2025). Sustainable artificial intelligence in finance: Impact of ESG factors. Frontiers in Artificial Intelligence, 8, Article 1566197. https://doi.org/10.3389/frai.2025.1566197 Goldstein, I., Jiang, W., & Karolyi, G. A. (2021). To FinTech and beyond. Review of Financial Studies, 34(7), 2967–3008. https://doi.org/10.1093/rfs/hhab020 Huang, Y., Li, X., & Zhang, H. (2025). Artificial intelligence, information environment, and capital market efficiency. Research in International Business and Finance, 79, 103094. https://doi.org/10.1016/j.ribaf.2025.103094 Hussain, M. A., Vemaraju, S., & Garg, A. (2025). The impact of AI tools on ESG-based sustainable banking practices. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3198 Jellouli, I. (2025). Rethinking portfolio design with sustainable AI and FinTech: Dynamic spillovers in a quantile–frequency framework. Cogent Economics & Finance, 13(1), Article 2568640. https://doi.org/10.1080/23322039.2025.2568640 Kotsantonis, S., & Serafeim, G. (2019). Four things no one will tell you about ESG data. Journal of Applied Corporate Finance, 31(2), 50–58. https://doi.org/10.1111/jacf.12346 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, N., Lohano, R. K., Asghar, M. M., Khalid, M., & Awan, D. (2025). Artificial intelligence and green finance: Unveiling the link to financial performance in Pakistan. Asian Bulletin of Green Management and Circular Economy, 5(1), 173–189. https://doi.org/10.62019/abgmce.v5i1.142 Liang, H., & Renneboog, L. (2020). Corporate social responsibility and sustainable finance. Journal of Corporate Finance, 62, 101597. https://doi.org/10.1016/j.jcorpfin.2020.101597 Liu, J., Yuan, Y., & Zhu, Z. (2026). The role of artificial intelligence in enhancing ESG disclosure quality in accounting. Journal of Risk and Financial Management, 19(1), 58. https://doi.org/10.3390/jrfm19010058 Mansour, M., Al Zobi, M., & Alomair, M. (2026). Artificial intelligence, ESG governance, and green innovation efficiency in emerging economies. Economies, 14(1), 11. https://doi.org/10.3390/economies14010011 Rafiq-uz-Zaman, M. (2025a). 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. (2025b). 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. (2025c). 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., & 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 Saltık, Ö. (2024). Algorithmic approaches to unraveling ESG impact on financial performance. International Journal of Pure and Applied Sciences, 10(2), 459–517. https://doi.org/10.29132/ijpas.1540178 Schwendner, P., & Posth, J. A. (2024). Trends in AI4ESG: AI for sustainable finance and ESG technology. Frontiers in Artificial Intelligence, 7, 1448045. https://doi.org/10.3389/frai.2024.1448045 Zhang, D., Mohsin, M., Rasheed, A. K., & Chang, Y. (2025). Green finance and artificial intelligence: Catalysts for promoting sustainability. Economic Analysis and Policy, 88, 13–25. https://doi.org/10.1016/j.eap.2025.08.037 Zhang, L., Wang, H., & Zhao, Y. (2025). Artificial intelligence and corporate investment efficiency: Evidence from China. Emerging Markets Review, 68, 101314. https://doi.org/10.1016/j.ememar.2025.101314 Zhang, X., Li, Y., & Chen, H. (2026). The impact of artificial intelligence on corporate ESG greenwashing. Socio-Economic Planning Sciences, 103, 102351. https://doi.org/10.1016/j.seps.2025.102351
Copyright (c) 2026 Dr. Syed Shameel Ahmed Quadri, Dr. Mehreen Ansari, Zonaira Akbar, Fahim Ullah Khan https://creativecommons.org/licenses/by-nc-sa/4.0
Public Trust in State Institutions: A Comparative Analysis of the Military, Judiciary, and Parliament in Post-2023 Election Pakistan
The foundation of democratic stability, institutional legitimacy, and effectiveness of the governance is public trust. In transitional democracies like Pakistan trust on the state institutions is often defined by political turbulence, judicial activism, civil-military relations as well as electoral contestation. The 2023 general elections became the most significant turning point in the history of political direction in Pakistan, which fueled the discussion of the stylistic legitimacy of the institution and trust between citizens. The paper explores the relative trust of the Pakistani population in three major pillars of the Pakistani state the military, judiciary and parliament and determines the socio-political conditions that impact the legitimacy of institutions in the post-election context. The study is based on a cross-sectional quantitative design through the analysis of the survey data of 714 respondents using stratified random sampling of the major urban districts. There was the use of statistical methods such as descriptive analysis, correlation matrices, one-way ANOVA, and multiple regression model to examine predictors of institutional trust. The results indicate that there is a strong trust asymmetry with the military receiving the greatest trust rating followed by the judiciary and the parliament correspondingly having lower trust ratings. The outcome of regression indicates that the perceived institutional performance, political polarization, media exposure, and corruption perception are relevant predictors of trust changes. Notably, the paper determines civic efficacy and democratic commitment as moderator factors that can alleviate distrust of representative institutions. The theoretical contribution to research is the synthesis of the Institutional Trust Theory and the Performance Legitimacy Frameworks, which shows that citizens are becoming more perceptive in the outcomes-based criterion instead of constitutional mandates alone when assessing institutional performance. This research finds that to restore confidence in the representative institutions, it would be necessary to introduce visible governance changes, transparency in its procedures and enhancement of accountability measures. Unless institutional balance is restored, chronic lack of trust can increase the weaknesses of democracy and entrench non-representative power. References Ahmed, S., & Asif, M. (2026a). 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 Ahmed, S., & Asif, M. (2026b). Comparative analysis of attitudes toward climate change policies across urban and rural populations. Pakistan Journal of Social Science Review, 5(1), 747–769. https://doi.org/10.5281/zenodo.18457821 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., Khan, A., & Pasha, M. A. (2019). Psychological capital of employees’ engagement: Moderating impact of conflict management in the financial sector of Pakistan. Global Social Sciences Review, 4(3), 160–172. https://doi.org/10.31703/gssr.2019(IV-III).15 Aurangzeb, Asif, M., & Amin, M. K. (2021). Resources management and SMEs’ performance. Humanities & Social Sciences Reviews, 9(3), 679–689. https://doi.org/10.18510/hssr.2021.9367 Bartolome, M. T., Fernandez, K., Cutipa-Luque, O., Tiahuallpa, Y., & Rojas, H. (2024). 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The Role of Maternal-Fetal Attachment and Psychological Well-being in Infant Neurodevelopment
This study examines the relationship between maternal psychological distress during the perinatal period, maternal-fetal attachment, and subsequent neurodevelopmental outcomes in infants. The historical reverence for motherhood in Azerbaijan faces contemporary challenges as limited awareness leaves expectant mothers vulnerable to psychological hardships during the perinatal period. The intrauterine environment is influenced not only by maternal nutrition but also by maternal emotional states, with implications for fetal neurodevelopment. A cross-sectional correlational study was conducted with 276 mothers of children diagnosed with various neurodevelopmental conditions, including Cerebral Palsy, Autism Spectrum Disorder, Down Syndrome, and Developmental Delay. Participants completed retrospective assessments of prenatal psychological distress, maternal-fetal attachment behaviours, and provided demographic and clinical information. Results of the 276 participants, 140 (50.7%) reported significant psychological distress during gestation. A statistically significant association was identified between prenatal distress and child's diagnostic category (χ² = 14.27, df = 5, p = .014). Mothers of children with Autism Spectrum Disorder reported the highest rates of prenatal distress (68.4%). Significant inverse relationships were observed between prenatal distress and maternal-fetal attachment behaviours, including emotional closeness to the fetus (r = -.42, p < .001). Findings demonstrate a significant correlation between maternal psychological distress during pregnancy and subsequent neurodevelopmental challenges in offspring. The results underscore the need for perinatal mental health screening and culturally appropriate intervention programs in Azerbaijan. References Ainsworth, M. D. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation. 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