The study identifies a gap in the understanding of the factors that shape the adoption
Research gap analysis derived from 3 social_science papers in our local library.
The gap
The study identifies a gap in the understanding of the factors that shape the adoption of AI in the energy sector. The study highlights the need for a socio-technical approach to the adoption of AI. The study suggests that prior work has fo
Evidence profile
Sourced from the stated research gap and abstract of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 64 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Overcoming Resistance to Change: Artificial Intelligence in the Energy Sector (2026) · Suranaree Journal of Social Science · doi
The study identifies a gap in the understanding of the factors that shape the adoption of AI in the energy sector. The study highlights the need for a socio-technical approach to the adoption of AI. The study suggests that prior work has focused on the technical aspects of AI adoption, neglecting the organizational and governance conditions.
generalstated research gapKeywords: study identifies gap understanding factors shape adoption energy - Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends (2024) · Cities · cited 64× · doi
• Maps the landscape of artificial intelligence (AI) adoption in local government (LG) • Advancements in the last decades brought the Blooming Era of AI adoption in LG • Main AI adoption purposes: decision support, automation, prediction, service delivery • Main AI adoption areas: planning, analytics, security, surveillance, energy, modelling • Under-researched but critical areas: responsibility and ethics of AI adoption…
generalabstractevidence 5/5Keywords: adoption main areas maps landscape artificial intelligence local government advancements last decades brought blooming purposes - AI governance and digital transformation in public utilities: Evidence from Morocco’s national electric grid (2026) · Applied Chemical Engineering · doi
The lack of understanding of how governance mechanisms condition the way institutional readiness for AI translates into internal acceptance of AI-enabled systems. The absence of empirical studies that integrate AI maturity, governance, and AI acceptability into a unified framework, particularly in monopolised energy services in emerging economies.
generalstated research gapevidence 5/5Keywords: lack understanding governance mechanisms condition way institutional readiness
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