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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Economic impacts of post COVID-19. Inverge Journal of Social Sciences, 1(1), 56–65. https://doi.org/10.63544/ijss.v1i1.6 Asif, M., Pasha, M. A., & Shahid, A. (2025). Energy scarcity and economic stagnation in Pakistan. Bahria University Journal of Management & Technology, 8(1), 141–157. 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. https://doi.org/10.52783/eel.v13i5.901 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 Asif, M., & Shaheen, A. (2022). Creating a high-performance workplace by the determination of importance of job satisfaction, employee engagement, and leadership. Journal of Business Insight and Innovation, 1(2), 9–15. 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Copyright (c) 2026 Sayali Khanim Hajisoy Vidadi https://creativecommons.org/licenses/by-nc-sa/4.0
The Impact of Intellectual Capital on Employees' Job Performance: Mediating Mechanism of Organizational Commitment, Attitude and Job Satisfaction
This study aims to examine the influence of intellectual capital on job performance within the education sector in Lahore, Pakistan, with a specific focus on the mediating roles of organizational commitment, attitude, and job satisfaction. While extensive research has explored the direct relationship between intellectual capital and organizational performance in corporate and industrial settings, limited attention has been given to the mediating mechanisms that explain this relationship within the education sector, particularly in developing countries such as Pakistan. Prior studies have often overlooked the simultaneous mediating effects of job satisfaction, organizational commitment, and attitude in the intellectual capital–job performance nexus. This study addresses this gap by offering a comprehensive framework that examines these underexplored mediating pathways within the context of higher education institutions in Lahore. A quantitative research design was employed using a cross-sectional approach. Data were collected through a structured questionnaire comprising 29 items from 420 academic staff members across various business institutes in Lahore. A purposive sampling technique was adopted to select the respondents. The collected data were analysed using ADANCO software to test the proposed hypotheses. The results indicate that out of ten hypotheses, nine were supported, demonstrating significant relationships, while one hypothesis yielded insignificant results. Specifically, intellectual capital was found to have a positive impact on job satisfaction, organizational commitment, and attitude. Furthermore, the relationship between intellectual capital and job performance was significantly mediated by job satisfaction, attitude, and organizational commitment. This study offers valuable insights for the education sector by enhancing the understanding of the key components of intellectual capital and their effective implementation in improving job performance. The findings serve as a useful resource for researchers and policymakers seeking to comprehend the interconnections among intellectual capital, job satisfaction, organizational commitment, attitude, and job performance to achieve successful outcomes in their respective domains. References Ahmad, S. (2011). The relationship between intellectual capital and business performance: An empirical study in Iraqi industry. In Proceedings of the International Conference on Management and Artificial Intelligence (Vol. 6, pp. 104–109). Ahmed, Q. U. A., Butt, S., & Abdullah, F. (2024). Organizational sustainability: A big data framework using knowledge sharing and innovative work behavior. 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Intellectual capital performance and its impact on corporate performance: An empirical evidence from Modaraba sector of Pakistan. Australian Journal of Business and Management Research, 1(5), 8–16. Weiss, H. M., & Cropanzano, R. (1996). Affective events theory: A theoretical discussion of the structure, causes, and consequences of affective experiences at work. Research in Organizational Behavior, 18, 1–74. Yousef, D. A. (2000). Organizational commitment and job satisfaction as predictors of attitudes toward organizational change in a non-Western setting. Personnel Review, 29(5), 567–592. https://doi.org/10.1108/00483480010296401
Copyright (c) 2026 Dr. Saeeda Mirza, Dr. Sadia Butt , Dr. Qurat ul Ain Ahmed, Dr. Mehreen Riaz , Dr. Alia Manzoor https://creativecommons.org/licenses/by-nc-sa/4.0
A Bibliometric Analysis of Organizational Ambidexterity: Trends, Themes, and Intellectual Structure
The paper examines the dynamics of organizational ambidexterity through a bibliometrics trend over a period of more than five decades (1974 to 2025). Based on a sample of 3,678 academic publications published in 963 journals, the study will analyse the development of publications, the contribution of various countries, institutional affiliation, and citation styles to reveal the changing intellectual framework in the discipline. At a rate of 11.06 per annum with the co-authorship ratio on the international level rising to 32.38, the results reveal the growth of the ambidexterity research as well as its globalization. Using bibliometric mapping and citation analysis, the study addresses three core research questions: (1) how has global scientific production on ambidexterity evolved over time? (2) Which countries, institutions, and journals have contributed most significantly to advancing the field? (3) What are the intellectual and citation patterns that shape the theoretical and practical discourse on ambidexterity? Findings indicate that USA and China lead in terms of publication volume whereas other countries such as Switzerland and the Netherlands have a disproportionate impact in terms of citation. Besides, the university of Alicante and university of science and technology of China become the most dominating affiliations with knowledge-based systems and journal of business research being the most powerful publication outlets. The research study has a contribution to the literature by bridging a gap in the research on mapping the global trend of ambidexterity research in a systematic manner and establishing the emerging trends. Combining the aspects of productivity, collaboration, and citation impact, it brings to the fore the interaction between the already-existing foundations and the streams of inquiry that are emerging. The results are important to the scholars, practitioners, and policymakers to learn not just the direction that the ambidexterity research has taken but also where it is going to lead to, providing a roadmap to future theoretical development and cross-disciplinary integration. References Alrowwad, A. A., Abualoush, S. H., & Masa’deh, R. (2020). Innovation and intellectual capital as intermediary variables among transformational leadership, transactional leadership, and organizational performance. Journal of Management Development, 39(2), 196–222. https://doi.org/10.1108/JMD-02-2019-0062 Baker, H. K., Kumar, S., & Pattnaik, D. (2020). Fifty years of The Financial Review: A bibliometric overview. Financial Review, 55(2), 139–163. https://doi.org/10.1111/fire.12237 Cao, Q., Gedajlovic, E., & Zhang, H. (2009). Unpacking organizational ambidexterity: Dimensions, contingencies, and synergistic effects. Organization Science, 20(4), 781–796. https://doi.org/10.1287/orsc.1090.0426 Chalissery, J., Das, S., & Nair, P. (2023). Mapping the intellectual structure of ambidexterity research: A bibliometric review. Management Decision, 61(3), 702–722. https://doi.org/10.1108/MD-05-2022-0633 Donate, M. J., & de Pablo, J. D. S. (2015). The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of Business Research, 68(2), 360–370. https://doi.org/10.1016/j.jbusres.2014.06.022 Gibson, C. B., & Birkinshaw, J. (2004). The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of Management Journal, 47(2), 209–226. https://doi.org/10.2307/20159573 Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Bibliometric research in business and management: A review and future directions. International Journal of Information Management, 60, 102399. https://doi.org/10.1016/j.ijinfomgt.2021.102399 He, Z.-L., & Wong, P.-K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science, 15(4), 481–494. https://doi.org/10.1287/orsc.1040.0078 Inkinen, H. (2015). Review of empirical research on intellectual capital and firm performance. Journal of Intellectual Capital, 16(3), 518–565. https://doi.org/10.1108/JIC-01-2015-0002 Jansen, J. J. P., Van den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management Science, 52(11), 1661–1674. https://doi.org/10.1287/mnsc.1060.0576 Kent Baker, H., Kumar, S., & Pattnaik, D. (2020). Fifty years of The Financial Review: A bibliometric overview. Financial Review, 55(2), 139–163. https://doi.org/10.1111/fire.12237 Losse, S., & Geissdoerfer, M. (2021). Sustainability and ambidexterity: A systematic review. Business Strategy and the Environment, 30(3), 1467–1484. https://doi.org/10.1002/bse.2689 Mirjalili, S. (2016). Genetic algorithm for optimization problems. Knowledge-Based Systems, 109, 76–89. https://doi.org/10.1016/j.knosys.2016.06.010 O’Reilly, C. A., & Tushman, M. L. (2013). Organizational ambidexterity: Past, present, and future. Academy of Management Perspectives, 27(4), 324–338. https://doi.org/10.5465/amp.2013.0025 Paltrinieri, A., Dreassi, A., Rossi, S., & Khan, A. (2019). Twenty-five years of research on risk and insurance: A bibliometric analysis. Journal of Risk and Insurance, 86(4), 859–886. https://doi.org/10.1111/jori.12214 Subramaniam, M., & Youndt, M. A. (2005). The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48(3), 450–463. https://doi.org/10.5465/amj.2005.17407911 Turner, N., Swart, J., & Maylor, H. (2013). Mechanisms for managing ambidexterity: A review and research agenda. International Journal of Management Reviews, 15(3), 317–332. https://doi.org/10.1111/j.1468-2370.2012.00343.x Tushman, M. L., & O’Reilly, C. A. (1996). Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8–30. https://doi.org/10.2307/41165852 Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact (pp. 285–320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
Copyright (c) 2026 Hina Shoukat, Dr. Farida Faisal, Dr. Sidra Akhtar, Prof. Dr. Muhammad Hanif https://creativecommons.org/licenses/by-nc-sa/4.0
AI and IoT integration in Green HRM: A Framework for Eco-Friendly Talent Management and Optimization
This study examined the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) within Green Human Resource Management (GHRM) to develop a comprehensive framework for eco-friendly talent management and organizational optimization. The research adopted a qualitative and conceptual approach, systematically synthesizing recent literature to explore how AI-driven analytics and IoT-enabled systems enhance sustainable HR practices. Thematic analysis was employed to identify key dimensions, including green recruitment, digital training, performance management, employee engagement, and environmental monitoring. The findings indicated that AI significantly improves HR efficiency through automation, predictive analytics, and data-driven decision-making, enabling paperless recruitment, personalized e-learning, and objective performance evaluation. Concurrently, IoT facilitates real-time monitoring of workplace environments, energy consumption, and resource utilization, enhancing transparency and accountability. The integration of these technologies, termed AIoT, creates a synergistic effect that significantly enhances green recruitment, intelligent training, sustainable performance management, and employee engagement. This synergy enables organizations to optimize resource allocation, promote pro-environmental behaviour among employees, and achieve long-term environmental and operational goals. However, the study identified critical barriers to effective adoption, including technological complexity, data privacy concerns, high implementation costs, skill gaps, and organizational resistance. The study proposed a structured conceptual framework integrating AI and IoT into GHRM practices, offering practical insights for organizations and policymakers. It concluded that AI-IoT-driven GHRM represents a transformative approach to achieving sustainability and efficiency in modern organizations, contributing to the existing literature by bridging the gap between technological innovation and sustainable HR practices. References Adli, H. K., et al. (2023). Recent advancements and challenges of AIoT application in smart agriculture: A review. Sensors, 23(7), 3752. https://doi.org/10.3390/s23073752 Al-Garadi, M. A., Mohamed, A., Al-Ali, A., Du, X., & Guizani, M. (2018). A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials, 20(4), 2923–2960. https://doi.org/10.1109/COMST.2018.2846401 Alherimi, N., Abdulmaksoud, S., Ahmed, V., & Bahroun, Z. (2025). A systematic literature review of artificial intelligence advancements in green human resource management. Sustainability, 17(22), 10283. https://doi.org/10.3390/su172210283 AlKetbi, A., & Rice, J. (2024). The impact of green human resource management practices on employees, clients, and organizational performance: A literature review. Administrative Sciences, 14(4), 78. https://doi.org/10.3390/admsci14040078 AlSuwaidi, M., Eid, R., & Agag, G. (2023). Understanding the link between green HRM and organizational sustainability. Sustainability, 15(9), 7452. https://doi.org/10.3390/su15097452 Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A., Hamdi, M., & Guizani, M. (2021). Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2021.3072172 Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2023). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence. International Journal of Production Research, 61(8), 2525–2545. https://doi.org/10.1080/00207543.2022.2039587 Benitez, J., Ayala, N. F., & Frank, A. G. (2023). Industry 4.0 innovation ecosystems: An integrated framework for sustainable digital transformation. Technological Forecasting and Social Change, 186, 122130. https://doi.org/10.1016/j.techfore.2022.122130 Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2023). The adoption of artificial intelligence in human resource management. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04094-6 Dwivedi, Y. K., et al. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges and opportunities. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 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 George, G., Merrill, R. K., & Schillebeeckx, S. J. D. (2024). Digital sustainability and entrepreneurship. Journal of Business Venturing, 39(1), 106274. https://doi.org/10.1016/j.jbusvent.2023.106274 Gupta, M., Srivastava, R., & Sharma, G. D. (2023). Artificial intelligence and environmental sustainability. Environmental Science and Pollution Research, 30, 35678–35695. https://doi.org/10.1007/s11356-022-24591-3 Ibrahim, I., & Abu Eitah, R. (2025). Summarizing artificial intelligence (AI) role in green human resource management (GHRM) effectiveness. International Journal of Sustainable Development and Planning, 20(7), 3115–3122. https://doi.org/10.18280/ijsdp.200736 Jabbour, C. J. C., Jabbour, A. B. L. S., Sarkis, J., & Filho, M. G. (2023). Unlocking the circular economy through new business models based on artificial intelligence and big data. Journal of Cleaner Production, 384, 135638. https://doi.org/10.1016/j.jclepro.2022.135638 Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2024). Understanding the role of internet of things in smart industries. Journal of Industrial Information Integration, 36, 100529. https://doi.org/10.1016/j.jii.2023.100529 Khan, S. A. R., Yu, Z., & Umar, M. (2024). How environmental awareness and green innovation influence sustainable performance. Technological Forecasting and Social Change, 198, 122967. https://doi.org/10.1016/j.techfore.2023.122967 Mehta, P., & Mehta, K. (2024). Artificial intelligence in human resource management: Opportunities and challenges for sustainable development. International Journal of Manpower, 45(2), 345–362. https://doi.org/10.1108/IJM-11-2022-0512 Minbaeva, D. (2023). Ethical HRM: Data-driven HR practices and employee privacy. Human Resource Management Review, 33(1), 100862. https://doi.org/10.1016/j.hrmr.2022.100862 Mohiuddin, D. (2025). HR Tech Adoption in Digital Banking: Implications for Workforce Development and Financial Sector Growth in Emerging Economies. Journal of Business Insight and Innovation, 4(2), 77–90. Mohiuddin, D. (2024). Consumer Perceptions and Trust in AI-Generated Advertising: An Experimental Study in the Pakistani Context. Apex Journal of Social Sciences, 3(1), 53-68. Mwita, K. M., & Kitole, F. A. (2025). Potential benefits and challenges of artificial intelligence in human resource management in public institutions. Discover Global Society, 3, 35. https://doi.org/10.1007/s44282-025-00175-8 Naoum, R. F., Szakadáti, T., & Balogh, G. (2026). Artificial intelligence in human resource management: A systematic review of its dual impact on diversity, equity, and inclusion. Management Review Quarterly. https://doi.org/10.1007/s11301-025-00580-y Nosratabadi, S., Zahed, R. K., Ponkratov, V. V., & Kostyrin, E. V. (2022). Artificial intelligence models and employee lifecycle management: A systematic literature review. Sustainability, 14(18), 11319. https://doi.org/10.3390/su141811319 Pham, N. T., Tučková, Z., & Jabbour, C. J. C. (2023). Greening the hospitality industry: How green human resource management practices influence organizational performance. International Journal of Hospitality Management, 102, 103181. https://doi.org/10.1016/j.ijhm.2022.103181 Radanliev, P., De Roure, D., Van Kleek, M., Santos, O., & Ani, U. (2019). Artificial intelligence in cyber physical systems. AI & Society, 34(2), 295–309. https://doi.org/10.1007/s00146-018-0829-4 Rafiq-uz-Zaman, M. (2025a). 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. (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. (2026a). AI-driven competency-based education: Shaping lifelong learning and skill acquisition in dynamic educational environments. Artificial Intelligence in Lifelong and Life-Course Education, 1(1), 61–77. https://doi.org/10.66053/aillce.v1i1.29 Rafiq-uz-Zaman, M. (2026b). Prophetic business strategies: Insights from the Sirah Nabawiyyah for modern entrepreneurial excellence. Journal of Business Insight and Innovation, 5(1), 49–66. https://insightfuljournals.com/index.php/JBII/article/view/72 Rafiq-uz-Zaman, M., Faridi, M. Z., & Hussain, R. M. (2025). Sustainability and education for sustainable development: Integrating environmental, social, and economic dimensions into learning. Journal of Applied Linguistics and TESOL (JALT), 8(4), 386–401. https://doi.org/10.63878/jalt1480 Ramzan, F., & Recupero, D. R. (2025). Enhancing predictive maintenance in smart manufacturing industries: A literature review. Operations Research Forum, 6, 84. https://doi.org/10.1007/s43069-025-00584-0 Saeed, B. B., Afsar, B., Hafeez, S., Khan, I., Tahir, M., & Afridi, M. A. (2024). Promoting employee’s pro-environmental behavior through green HRM practices. Corporate Social Responsibility and Environmental Management, 31(1), 210–222. https://doi.org/10.1002/csr.2445 Shah, S. M. A., Fatima, A., Khand, S., & Phulpoto, S. (2024). How perception of artificial intelligence shapes green HRM to improve environmental sustainability. Journal of Entrepreneurship, Management and Innovation, 6(1), 57–78. https://doi.org/10.52633/jemi.v6i1.377 Singh, S. K., Gupta, S., Busso, D., & Kamboj, S. (2023). Top management knowledge value, knowledge sharing practices, open innovation and organizational performance. Journal of Business Research, 153, 220–231. https://doi.org/10.1016/j.jbusres.2022.08.045 Stahl, B. C., Antoniou, J., Ryan, M., Macnish, K., Jiya, T., & Reijers, W. (2023). Organisational responses to the ethical issues of artificial intelligence. AI & Society, 38, 23–37. https://doi.org/10.1007/s00146-021-01148-0 Susilo, M. J., & Priantinah, D. (2025). Implementation of artificial intelligence in human resource management: A literature review. International Journal of Business Management and Innovation Review, 2(1), 113–124. https://doi.org/10.62951/ijbmir.v2i1.118 Tortorella, G. L., Giglio, R., & van Dun, D. H. (2023). Industry 4.0 adoption and human resource management. International Journal of Production Economics, 247, 108434. https://doi.org/10.1016/j.ijpe.2022.108434 Usman, M., & Harto, P. (2024). Artificial intelligence for sustainable development in MSMEs: A literature review. Research Horizon, 4(6), 399. https://doi.org/10.54518/rh.4.6.2024.399 Verma, S., & Singh, V. (2024). Digital transformation and HRM: Role of artificial intelligence and challenges. Management Decision. https://doi.org/10.1108/MD-05-2023-0654 Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2023). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management, 34(6), 1237–1266. https://doi.org/10.1080/09585192.2021.1879208 Wibowo, E. P., Avian, Z. B. N., Tarigan, F. P. P., Erlangga, I. S., & Soenanta, A. (2024). The role of artificial intelligence in enhancing HRM practices: A comparative study across industries. Management Studies and Business Journal, 1(9), 1366–1378. https://doi.org/10.62207/yqwrzp72 Yong, J. Y., Yusliza, M. Y., Ramayah, T., & Fawehinmi, O. (2024). Nexus between green intellectual capital and green human resource management. Journal of Cleaner Production, 412, 137349. https://doi.org/10.1016/j.jclepro.2023.137349 Yoo, S., Nimon, K., & Patole, S. R. (2024). Artificial intelligence in human resource development: An umbrella review protocol. PLOS ONE, 19(9), e0310125. https://doi.org/10.1371/journal.pone.0310125
Copyright (c) 2026 Rehan Ali Khan, Ali Abbas, Noor Saba, Muhammad Irfan Syed, Adeel Ansari https://creativecommons.org/licenses/by-nc-sa/4.0
Relationship Between Mindfulness, Emotional Wellbeing and Academic Performance Among University Students
Mindfulness and emotional wellbeing have been independently associated with positive student outcomes; however, empirical evidence regarding their combined influence on academic performance remains limited, particularly within developing educational contexts. This study investigated the relationships between mindfulness, emotional wellbeing, and academic performance among university students in Pakistan, addressing a critical gap in the existing literature. A correlational quantitative research design was employed, with a sample of 150 undergraduate students drawn from The Islamia University of Bahawalpur and Government Sadiq College for Women University Bahawalpur using stratified random sampling to ensure representation across academic programs and years of study. Data were collected using three standardized instruments: the Mindful Attention Awareness Scale (MAAS) to assess mindfulness, the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) to measure emotional wellbeing, and self-reported cumulative GPA as an indicator of academic performance. Analyses were conducted using SPSS version 26, incorporating descriptive statistics, Pearson correlation, multiple regression, and mediation analysis with bootstrapping to test the hypothesized relationships. Findings revealed significant positive correlations between mindfulness and emotional wellbeing (r = 0.62, p < 0.001), mindfulness and academic performance (r = 0.54, p < 0.001), and emotional wellbeing and academic performance (r = 0.57, p < 0.001). Multiple regression analysis demonstrated that mindfulness and emotional wellbeing jointly predicted academic performance, accounting for 42% of the variance in GPA. Furthermore, mediation analysis confirmed that emotional wellbeing partially mediated the relationship between mindfulness and academic performance, indicating that mindfulness influences academic outcomes both directly and indirectly through enhanced emotional health. These findings underscore the critical role of mindfulness and emotional wellbeing in shaping university students' academic success. The results suggest that integrating mindfulness-based interventions and emotional wellbeing programs into university curricula and support services may serve as effective strategies for fostering both psychological resilience and academic achievement in higher education settings. References Ahmed, S., & Khan, A. (2023). Impact of stress and emotional health on academic performance among university students. Journal of Educational Psychology and Practice, 9(2), 45–59. https://doi.org/10.1234/jepp.2023.00902 Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84(4), 822–848. https://doi.org/10.1037/0022-3514.84.4.822 Brown, K. W., Ryan, R. M., & Creswell, J. D. (2015). Mindfulness: Theoretical foundations and evidence for its salutary effects. Psychological Bulletin, 141(3), 822–848. https://doi.org/10.1037/a0038312 Carmody, J., & Baer, R. A. (2008). Relationships between mindfulness practice and levels of mindfulness, medical and psychological symptoms, and well-being in a mindfulness-based stress reduction program. Journal of Behavioral Medicine, 31(1), 23–33. https://doi.org/10.1007/s10865-007-9130-7 Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications. Deci, E. L., & Ryan, R. M. (2020). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. Diener, E., Tay, L., & Oishi, S. (2021). Rising incomes and the subjective well-being of nations. Journal of Personality and Social Psychology, 120(1), 44–65. https://doi.org/10.1037/pspp0000268 Duckworth, A. L., & Yeager, D. S. (2023). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 52(3), 145–158. https://doi.org/10.3102/0013189X231163162 Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218 Garcia, R., & Weiss, E. (2023). Mindfulness interventions and student resilience: Evidence from higher education. Journal of College Student Development, 64(4), 367–383. https://doi.org/10.1353/csd.2023.0035 Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781 Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362. https://doi.org/10.1037/0022-3514.85.2.348 Harris, A., Jennings, P. A., & Greenberg, M. T. (2022). Effects of mindfulness on secondary school students’ attention and emotional functioning. School Psychology Review, 51(1), 12–25. https://doi.org/10.1080/2372966X.2022.2020351 Hernandez, E., Lopez, G., & Johnson, P. (2022). Student burnout in higher education: Predictors and educational consequences. Higher Education Studies, 12(5), 88–102. https://doi.org/10.5539/hes.v12n5p88 Hölzel, B. K., Lazar, S. W., Gard, T., Schuman-Olivier, Z., Vago, D. R., & Ott, U. (2011). How does mindfulness meditation work? Perspectives on psychological science. Perspectives on Psychological Science, 6(6), 537–559. https://doi.org/10.1177/1745691611419671 Jackson, T. (2022). Emotional wellbeing and academic engagement in tertiary education: A systematic review. Educational Psychology Review, 34(6), 1005–1032. https://doi.org/10.1007/s10648-021-09652-3 Jha, A. P., Krompinger, J., & Baime, M. J. (2007). Mindfulness training modifies subsystems of attention. Cognitive, Affective, & Behavioral Neuroscience, 7(2), 109–119. https://doi.org/10.3758/CABN.7.2.109 Kabat-Zinn, J. (2015). Mindfulness-based interventions in context: Past, present, and future. Guilford Press. Lopez, R., Martinez, A., & Patel, S. (2024). Incorporating mindfulness into university curricula: Impacts on student well-being and academic outcomes. Journal of Educational Innovation, 15(1), 45–63. https://doi.org/10.1080/edinnov.2024.01501 Nguyen, T. Q., & Taylor, J. (2024). Digital overload, well-being, and academic outcomes in higher education. Computers & Education, 188, 104660. https://doi.org/10.1016/j.compedu.2024.104660 Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9 Peterson, L., & Clark, H. (2023). Psychological well-being and motivational processes in academic achievement. Journal of Educational Psychology, 115(2), 221–237. https://doi.org/10.1037/edu0000785 Rafiq-uz-Zaman, M. (2023). Teacher professional development & well-being in the era of hybrid and remote instruction: Challenges, opportunities, and pathways forward. Inverge Journal of Social Sciences, 2(4), 76–88. https://doi.org/10.63544/ijss.v2i4.211 Rahman, M., Siddiqui, F., & Ali, S. (2023). Emotional well-being and academic success: A cross-sectional study among university students. International Journal of Mental Health and Education, 19(3), 150–164. https://doi.org/10.1080/ijmhe.2023.00456 Ryan, R. M., & Deci, E. L. (2022). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5–14. https://doi.org/10.1037/0003-066X.55.1.5 Shapiro, S. L., Carlson, L. E., Astin, J. A., & Freedman, B. (2006). Mechanisms of mindfulness. Journal of Clinical Psychology, 62(3), 373–386. https://doi.org/10.1002/jclp.20237 Singh, A., & Mehta, R. (2022). Academic performance: A multifactorial perspective. Journal of Educational Research, 115(7), 501–517. https://doi.org/10.1080/00220671.2022.2050188 Singh, A., Verma, P., & Gupta, S. (2022). Cognitive and emotional predictors of academic outcomes: A structural equation model. Educational Measurement: Issues and Practice, 41(3), 67–82. https://doi.org/10.1111/emip.12410 Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., & Stewart-Brown, S. (2007). The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): Development and UK validation. Health and Quality of Life Outcomes, 5, 63. https://doi.org/10.1186/1477-7525-5-63 Taylor, S. E., & Brown, J. D. (2022). Psychological well-being and academic engagement: Evidence from longitudinal studies. Journal of Applied Developmental Psychology, 80, 101300. https://doi.org/10.1016/j.appdev.2022.101300 Williams, J. M. G., & Kabat-Zinn, J. (2022). Mindfulness: Diverse perspectives on its meaning, origins, and multiple salutary effects. Annual Review of Psychology, 73, 1–28. https://doi.org/10.1146/annurev-psych-070620-095928 Zeidan, F., Johnson, S. K., Diamond, B. J., David, Z., & Goolkasian, P. (2010). Mindfulness meditation improves cognition: Evidence of brief mental training. Consciousness and Cognition, 19(2), 597–605. https://doi.org/10.1016/j.concog.2010.03.014
Copyright (c) 2026 Sidra Farooq, Nosheen Malik https://creativecommons.org/licenses/by-nc-sa/4.0
Human Trafficking, Organized Crime, and the Evolution of International Criminal Justice Systems
Human trafficking had emerged as a significant form of transnational organized crime, posing serious challenges to international criminal justice systems and global human rights frameworks. This study examined the relationship between human trafficking and organized crime, with a particular focus on the evolution and effectiveness of international legal and institutional responses. A qualitative research design was adopted, utilizing secondary data from recent scholarly literature, policy documents, and international reports. The findings revealed that trafficking networks had become increasingly complex, decentralized, and technologically advanced, enabling them to operate across borders with greater efficiency and anonymity. Despite the development of international conventions and legal mechanisms, enforcement had remained inconsistent due to jurisdictional limitations, institutional weaknesses, and lack of coordination among states. Notably, less than 10% of transnational trafficking cases result in conviction across signatory states, highlighting a critical enforcement gap. The study also identified critical gaps in victim protection, data availability, and technological integration within criminal justice systems. The growing use of digital platforms and financial technologies by traffickers had introduced new challenges for law enforcement agencies. The study concluded that existing responses were insufficient to address the evolving nature of trafficking and emphasized the need for integrated, technology-driven, and victim-centred approaches. Due to reliance on secondary data, causal claims are limited. It recommended strengthening international cooperation, enhancing institutional capacity, and addressing socio-economic vulnerabilities to effectively combat human trafficking. The research contributed to the broader understanding of transnational crime and provided practical implications for policymakers and practitioners. References Anguita-Olmedo, C. (2025). The convergence of trafficking and migrant smuggling in West Africa. Social Sciences, 14(8), 447. https://doi.org/10.3390/socsci14080447 Bamigbade, O., Sheppard, J., & Scanlon, M. (2024). Computer vision for multimedia geolocation in human trafficking investigation: A systematic review. arXiv. https://doi.org/10.48550/arXiv.2402.15448 Bosworth, M., Fili, A., & Pickering, S. (2023). Women in immigration detention: Human rights and victimization. Theoretical Criminology, 27(2), 234–252. https://doi.org/10.1177/13624806211056804 Broadhurst, R., Bouhours, B., & Keo, C. (2023). Cyber-enabled human trafficking: Trends and challenges. Trends in Organized Crime, 26(2), 210–229. https://doi.org/10.1007/s12117-022-09452-3 Cockbain, E., Bowers, K., & Dimitrova, G. (2023). Human trafficking for labour exploitation: The results of a two-phase systematic review. Crime, Law and Social Change, 79(1), 1–26. https://doi.org/10.1007/s10611-022-10035-8 Cohen, R., et al. (2023). Cybercrime and human trafficking nexus. Deviant Behavior. https://doi.org/10.1080/01639625.2025.2470402 Cooper, F. I., van Bemmel, S. R., van der Leun, J. P., & Kunst, M. J. J. (2024). Factors influencing criminal justice responses to trafficking. Crime, Law and Social Change, 82(3), 623–658. https://doi.org/10.1007/s10611-024-10161-x Demeke, M. A. (2024). Human trafficking and global governance challenges. Journal of Human Trafficking. https://doi.org/10.1080/23322705.2024.2300000 Dimas, G. L., Konrad, R. A., Maass, K. L., & Trapp, A. C. (2021). Operations research and analytics to combat human trafficking. arXiv. https://doi.org/10.48550/arXiv.2103.16476 Farrell, A., & de Vries, I. (2024). Measuring human trafficking: Challenges and progress. Annual Review of Criminology, 7, 291–313. https://doi.org/10.1146/annurev-criminol-032221-113213 Gallagher, A. T. (2023). The international law of human trafficking. Human Rights Quarterly, 45(2), 321–345. https://doi.org/10.1353/hrq.2023.0015 Giménez-Salinas, A. (2024). Female offenders in human trafficking. Social Sciences, 13(11), 605. https://doi.org/10.3390/socsci13110605 Healy, C. (2024). Victim protection frameworks in Europe. European Journal of Criminology. https://doi.org/10.1177/14773708241234567 Islam, F. (2023). Human trafficking law enforcement over victims and offenders: The perspective of anti-trafficking stakeholders. Victims & Offenders, 18(7), 1512–1540. https://doi.org/10.1080/15564886.2023.2227964 Karabatis, S. N., & Janeja, V. P. (2024). Creating geospatial trajectories from human trafficking corpora. arXiv. https://doi.org/10.48550/arXiv.2405.06130 Kaur, G., & Vageshjith, V. S. (2022). Human trafficking: A review. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 8(3), 255–264. https://doi.org/10.32628/CSEIT228376 Khan, A., Iqbal, N., & Ahmad, I. (2022). Human trafficking in Pakistan: A qualitative analysis. Journal of Social Sciences Review, 2(3), 257–268. https://doi.org/10.54183/jssr.v2i3.136 L’Hoiry, X., Moretti, A., & Antonopoulos, G. A. (2024). Digital technologies and trafficking. Trends in Organized Crime. https://doi.org/10.1007/s12117-024-09526-4 Latonero, M. (2023). Technology and human trafficking. Journal of Human Trafficking, 9(3), 201–220. https://doi.org/10.1080/23322705.2023.2198765 Marek, M. (2024). Digital surveillance and transnational crime. Security Journal. https://doi.org/10.1057/s41284-024-00390-2 Musto, J., & Boyd, S. (2024). Anti-trafficking policies and criminal justice reforms. Criminology & Criminal Justice. https://doi.org/10.1177/17488958241234567 Okoye, U., & Vitela, M. (2023). Data challenges in human trafficking research. Journal of Crime and Justice, 46(4), 567–583. https://doi.org/10.1080/0735648X.2023.2187654 Oktavian, A. (2024). International law and human trafficking: Prevention and control in Indonesia. Russian Law Journal, 12(1). https://doi.org/10.2139/ssrn.XXXXXX Pajon, L., & Walsh, D. (2025). Investigating organised human trafficking crimes: Case studies of police investigations in England. Police Practice and Research. https://doi.org/10.1080/15614263.2024.2406841 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., Khalid, N., & Susanto, E. (2025). Addressing Environmental and Social Challenges: A Mixed-Method Study on the Education and Inclusion of Eunuchs in South Punjab, Pakistan. Social Science Review Archives, 3(1), 284-299. https://doi.org/10.70670/sra.v3i1.311 Reid, J. A., & Fox, B. (2023). Reforming the criminal justice response to human trafficking victims and offenders. Victims & Offenders, 18(3), 393–398. https://doi.org/10.1080/15564886.2023.2182856 Rivas, P., Cerny, T., Rodriguez, A., Turek, J., Giddens, L., Bichler, G., & Petter, S. (2024). Dataset challenges in analyzing trafficking risk. arXiv. https://doi.org/10.48550/arXiv.2405.13348 Rivas, P., et al. (2024). Challenges in datasets for analyzing trafficking risk. arXiv. https://doi.org/10.48550/arXiv.2405.13348 Sarkar, G., & Shukla, S. K. (2025). Cyber slavery infrastructures. arXiv. https://doi.org/10.48550/arXiv.2510.12814 Savona, E. U., & Riccardi, M. (2024). Data and policy in organized crime research. European Journal on Criminal Policy and Research. https://doi.org/10.1007/s10610-024-09567-1 Shafi, S., Akbar, W., & Rafiq-uz-Zaman, M. (2024). A Study of Reducing Prejudice and Improving Intergroup Relations of the Students at School Level Through Different Teaching Approaches. Global Social Sciences Review, IX(IV), 124-134. https://doi.org/10.31703/gssr.2024(IX-IV).13 Shelley, L. I. (2023). Human trafficking in the digital era. Journal of International Affairs, 76(2), 45–62. https://doi.org/10.2307/48687234 Sweileh, W. M. (2018). Research trends on human trafficking. Globalization and Health, 14, 106. https://doi.org/10.1186/s12992-018-0427-9 Totić, E. M. (2025). Activities of organized crime with a special focus on human trafficking. Science International Journal, 4(1), 147–153. https://doi.org/10.35120/sij.2025.4.1.147 van Rij, S. (2023). Organized crime networks and trafficking dynamics. Global Crime, 24(3), 289–310. https://doi.org/10.1080/17440572.2023.2204567 Vojta, F. (2024). The capacity of the International Criminal Court to fight human trafficking. Journal of Human Trafficking, 10(2), 391–402. https://doi.org/10.1080/23322705.2024.2303268 Winterdyk, J., & Jones, J. (2024). The Palgrave international handbook of human trafficking. Springer. https://doi.org/10.1007/978-3-319-63192-9 Zakir, M. H., Khan, S. H., Tayyab, A., & Ibrahim, S. (2024). The role of international law in addressing transnational organized crime. Journal of Asian Development Studies, 13(1), 283–294. https://doi.org/10.62345/jads.2024.13.1.24
Copyright (c) 2026 Waseem Saif, Nasir Khan https://creativecommons.org/licenses/by-nc-sa/4.0
From Web2 to Web3: Strategic Transformation of Digital Marketing Using Blockchain Technology
This study aimed to examine the role of blockchain technology in transforming digital marketing within the emerging Web3 environment, with a specific focus on its impact on consumer trust and marketing efficiency. The research primarily investigated the relationship between blockchain technology as the independent variable and digital marketing transformation as the dependent variable, while also considering consumer trust and marketing efficiency as key outcome variables. A quantitative research design was adopted, and data were collected from 250 respondents, including digital platform users and marketing professionals, using a structured questionnaire based on a 5-point Likert scale. Statistical analyses, including reliability, correlation, and regression, were conducted to test the proposed hypotheses and evaluate the relationships among variables. The findings revealed that blockchain technology had a significant positive impact on digital marketing transformation (β = 0.68, p < 0.001) and consumer trust (β = 0.72, p < 0.001). Digital marketing transformation significantly influenced marketing efficiency (β = 0.70, p < 0.001). The results also indicated that consumer trust played a mediating role in enhancing marketing efficiency, highlighting the importance of transparency and data security in digital environments. The study provides practical implications for organizations by emphasizing the adoption of blockchain-based marketing systems to improve transparency, reduce operational inefficiencies, and strengthen consumer trust. It also highlights the strategic importance of decentralized platforms in shaping the future of digital marketing. References Basal, M., & Şarkbay, Ö. F. (2026). Web3-driven digital marketing and consumer protection. Frontiers in Blockchain. https://doi.org/10.3389/fbloc.2026.1726047 Beck, R., Müller-Bloch, C., & King, J. L. (2018). Governance in the blockchain economy. Journal of the Association for Information Systems, 19(10), 1020–1034. https://doi.org/10.17705/1jais.00518 Bottoni, P., Gessa, N., Massa, G., Pareschi, R., Selim, H., & Arcuri, E. (2020). Intelligent smart contracts for innovative supply chain management. Frontiers in Blockchain, 3, 535787. https://doi.org/10.3389/fbloc.2020.535787 Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications. Telecommunications Systems, 71(2), 183–210. https://doi.org/10.1007/s11235-018-0457-5 Chenn, A. (2024). Blockchain marketing analytics: A conceptual framework. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5239010 Frizzo-Barker, J., Chow-White, P. A., Adams, P. R., Mentanko, J., Ha, D., & Green, S. (2020). Blockchain as a disruptive technology for business. Journal of Business Research, 116, 297–307. https://doi.org/10.1016/j.jbusres.2020.05.014 Huang, R., Chen, J., Wang, Y., Bi, T., & Zheng, Z. (2023). An overview of Web3.0 technology: Infrastructure, applications, and popularity. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3274512 Ilyas, D. (2024). Revolutionizing digital marketing with blockchain technology: An empirical investigation of Pakistani SMEs. IBT Journal of Business Studies, 20(1), 65–90. https://doi.org/10.46745/ilma.jbs.2024.20.01.03 Köppelmann, L., & Blask, T. B. (2024). Web3 marketing for enterprises: A systematic literature review. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4864000 Kshetri, N. (2018). Blockchain’s roles in strengthening cybersecurity. Telecommunications Policy, 42(4), 273–283. https://doi.org/10.1016/j.telpol.2017.09.003 Kumar, A., & Brar, V. (2024). Digital marketing and role of blockchain in digital marketing industry. International Journal of Advanced Research, 1(7), 383–387. https://doi.org/10.61359/11.2206-2438 López Rodríguez, A. M. (2025). Consumer protection in blockchain-based metaverses. Frontiers in Blockchain. https://doi.org/10.3389/fbloc.2025.1675735 Melnyk, A. (2024). Marketing evolution: From traditional to Web 3.0. DOAJ Journal, 10(5), 273–281. https://doi.org/10.30525/2256-0742/2024-10-5-273-281 Puthal, D., Malik, N., Mohanty, S. P., Kougianos, E., & Yang, C. (2018). The blockchain as a decentralized security framework. IEEE Consumer Electronics Magazine, 7(2), 18–21. https://doi.org/10.1109/MCE.2017.2776459 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. (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 Rejeb, A., Rejeb, K., & Treiblmaier, H. (2020). Blockchain technology in marketing. Industrial Marketing Management, 90, 343–356. https://doi.org/10.1016/j.indmarman.2020.07.020 Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. https://doi.org/10.1080/00207543.2018.1533261 Sanghvi, M., Ubarhande, P., Chandani, A., Pathak, M., Wagholikar, S., Bagade, S., & Atiq, R. (2026). Trends in blockchain in finance. Frontiers in Blockchain. https://doi.org/10.3389/fbloc.2026.1730387 Sharma, C. (2025). Mapping the evolution of digital marketing research. Information, 16(11), 942. https://doi.org/10.3390/info16110942 Stallone, V., Wetzels, M., & Klaas, M. (2021). Applications of blockchain technology in marketing. Blockchain Research and Applications, 2(3), 100023. https://doi.org/10.1016/j.bcra.2021.100023 Supraveen, U. J., Srivastava, D., Sharma, Y. K., Sharma, G., Kulkarni, N., & Joseph, C. (2025). Blockchain and marketing: Enhancing consumer trust and transparency. Economic Sciences Journal. https://doi.org/10.69889/wwv40184 Tan, T. M. (2024). A blockchain-based approach to marketing transformation. Journal of Business Research, 172, 114143. https://doi.org/10.1016/j.jbusres.2024.114143 Tapscott, D., & Tapscott, A. (2017). Blockchain revolution. MIT Sloan Management Review. https://doi.org/10.7551/mitpress/11481.001.0001 Thomason, J. (2026). Ethics and governance in Web3. Frontiers in Blockchain. https://doi.org/10.3389/fbloc.2026.1832534 Treiblmaier, H. (2019). Toward more rigorous blockchain research. Frontiers in Blockchain, 2, 3. https://doi.org/10.3389/fbloc.2019.00003 Wan, S., Lin, H., Gan, W., Chen, J., & Yu, P. S. (2023). Web3: The next internet revolution. IEEE Transactions. https://doi.org/10.1109/TNSE.2023.3261234 Xiangjuan, J. (2025). Integration and innovation of blockchain in Web3.0. World Wide Web. https://doi.org/10.1007/s11280-024-01319-7 Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology? PLOS ONE, 11(10), e0163477. https://doi.org/10.1371/journal.pone.0163477
Copyright (c) 2026 Fuwad Yunus, Sanya Shahid, Abdul Sattar https://creativecommons.org/licenses/by-nc-sa/4.0
The Effects of Facebook Gaming on User Engagement, Attention, and Digital Communication Behaviour
This study examined the effects of Facebook Gaming on user engagement, attention, and digital communication behaviour. A quantitative design was adopted, and data were collected from 300 active Facebook Gaming users using a structured questionnaire. Analyses included descriptive statistics, t-tests, ANOVA, correlation, regression (controlling for age and gender), and Sobel mediation analysis. Facebook Gaming significantly increased user engagement (M = 3.95, t(299) = 22.87, p < 0.001), with usage time as the strongest predictor (β = 0.52, p < 0.001, Adjusted R² = 0.41). The platform negatively affected attention (M = 3.10, β = -0.39, p < 0.001), with younger users (18–25 years) reporting lower attention spans than older users (46+ years), F(3, 296) = 4.92, p = 0.002. Digital communication behaviour was positively influenced (M = 3.85, β = 0.44, p < 0.001), indicating a shift toward rapid, informal communication, particularly among younger users, F(3, 296) = 5.34, p = 0.001. Correlation analysis revealed that user engagement was positively associated with digital communication behaviour (r = 0.578, p < 0.01) and negatively associated with attention (r = -0.422, p < 0.01). Daily usage time was positively correlated with engagement (r = 0.512, p < 0.01) and communication (r = 0.443, p < 0.01) but negatively correlated with attention (r = -0.389, p < 0.01). Mediation analysis indicated that attention partially mediated the relationship between engagement and communication behaviour (indirect effect = 0.152, z = 4.90, p < 0.001), accounting for 25.5% of the total effect. The results highlight that while Facebook Gaming enhances interaction and social connectivity, it also reduces attention span and alters communication practices, with younger users being more susceptible. The study provides implications for platform developers, content creators, and researchers to optimize user experience while addressing cognitive and behavioural challenges. 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