Policy Significance From a policy perspective, the study

The gap

Evidence profile

Sourced from the limitations and recommendations and future work and stated research gap of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• Artificial Intelligence and the Future of International Trade Law: Balancing Innovation, Regulation, and Global Fairness (2026) · Legalis : Journal of Law Review · doi

  advances to the When interpreting the results of this study, several points should be addressed. The research is primarily a quantitative survey, focusing on the United States. This implies that its findings may not be generalizable to other geographies with different regulatory environments, commercial business models and AI adoption levels. Another limitation of our study is that all information was reported by the interviewees themselves, so biases such as social desirability bias or recall bias could potentially the these results. The paper contributes affect governance and ethical issues of AI in trade law but overlooks technical challenges associated with deployment of AI systems in trading flows, such as: data quality; transparency of algorithms; and their consistently and constantly integration into trade existing eco-systems. The researcher's potential biases will be an inevitable problem for future studies to overcome, as that same from a wider geographic research should select distribution of at least some countries or regions in order to develop a superior scope for AI’s effect on international trade law. Other qualitative methods such as in-depth interviews or case studies can provide a more granular and comprehensive understanding of the consequences of AI adoption. It would be interesting to analyze the technical issues related to AI applications across global commerce chains, mainly challenges of unified implementation and data sharing among many AI systems. Future research should explore the long-term effects of AI on trade law and how evolving technology and regulation will shape the global trading system in the years ahead. That would provide greater understanding about how AI might also shape international trade in the future and beyond.

  generallimitationsevidence 5/5
  Keywords: trade systems future adoption biases bias issues technical challenges trading international provide understanding global shape
• GEOPOLITICS OF ARTIFICIAL INTELLIGENCE: ASSESSING PAKISTAN'S STRATEGIC AUTONOMY IN THE EMERGING GLOBAL AI POWER COMPETITION (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Policy Significance From a policy perspective, the study offers for evidence-based strengthening national AI strategies, improving digital sovereignty, and reducing technological dependency. It highlights the need for long-term indigenous AI development, investment regulatory frameworks for data governance, and international partnerships that enhance rather than compromise strategic autonomy.

  generalrecommendationsevidence 5/5
  Keywords: policy significance perspective offers evidence based strengthening national strategies improving digital sovereignty reducing technological dependency
• DIGITAL TRANSFORMATION AND GOVTECH: INSIGHTS FROM ARIMA MODELLING AND FORECAST ANALYSIS (2026) · Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi

  The literature is still fragmented; empirical studies are often limited to the national level, and comparative and multi-actor analyses are needed (Liva et al. (2020). Studies on AI, algorithmic governance and ethics are insufficient. “AI-first governance” needs to be theorized (Balaji, 2025). Emerging trends—such as AI integration, blockchain expansion, and open data initiatives—signal GovTech’s evolving role (OECD, 2023). Policymakers must prioritize digital inclusion, robust security frameworks, and change management to maximize benefits. For Turkey, addressing regional disparities and scaling smart city initiatives could amplify impact (World Bank, 2022). This study offers some solid insights, but there are still plenty of research paths to explore: Bringing in External Factors: The models don’t take into account outside influences like political stability, public investment in AI, or changes in cybersecurity policies. By incorporating these elements into extended time-series models (like ARIMAX) could enhance understanding of causality and make findings more relevant for policymakers. Blending Methods with Qualitative Case Studies: It’s important to pair quantitative findings with qualitative insights, such as surveys on citizen satisfaction, evaluations of administrative capacity, or analyses of leadership. This is particularly vital for grasping situations where EGDI scores are high, yet the actual delivery of digital services falls short. Sensitivity to Disruptions Post-2024: Future studies should look into simulations that involve external shocks, like data breaches, incidents of algorithmic bias, or significant governance reforms. This would help us evaluate how resilient digital systems and GovTech strategies are over time. Exploring these avenues would not only tackle the limitations of this study but also enhance the theoretical and practical frameworks that will guide the next stage of global digital transformation. 15 REFERENCES Balaji, K. (2025). E-Government and E-Governance: Driving Digital Transformation in Public Administration. Public Governance Practices in the Age of AI, 23-44. https://doi. org/10.4018/979-8-3693-9286-7.ch002. Bates, J. (2012). This is what modern deregulation looks like: Co-optation and contestation in the shaping of the UK’s Open Government Data Initiative. The Journal of Community Informatics, 8(2), 1–20. Carter, L., & Belanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5–25. https://doi. org/10.1111/j.1365-2575.2005.00183.x. Cordella, A., & Tempini, N. (2015). E-government and organizational change: Reappraising the role of ICT in public sector reform. Government Information Quarterly, 32(3), 279–286. https://doi.org/10.1016/j.giq.2015.06.002. Drèze, J., & Khera, R. (2017, August 10). The Aadhaar debate. The Indian Express.

  generalfuture workevidence 5/5
  Keywords: governance digital government like public https still analyses algorithmic balaji open initiatives govtech role policymakers
• Potential negative effects of artificial intelligence in Kazakhstan’s public sector: an analysis of hidden risks (2026) · Frontiers in Artificial Intelligence · doi

  The study identifies a research gap in the application of socio-technical 'dark side' and AI governance frameworks to CIS digital states. The study notes that the CIS and Kazakhstan-focused literature is analytically segmented and does not reconstruct how design-level choices in AI legislation, integrated data infrastructures, and public-sector platforms jointly institutionalize risks ex ante.

  generalstated research gapevidence 5/5
  Keywords: study identifies research gap application socio-technical dark side

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