SynthesisF1000Research2025
Artificial Intelligence Adoption as a Driver of Innovation and Competitiveness in SMEs: A Bibliometric and Systematic Review.
Synthesis in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study employs a bibliometric and systematic literature review (B-SLR) approach, guided by the PRISMA 2021 framework, to investigate the transformative impact of Artificial Intelligence (AI) on small and medium-sized enterprises (SMEs). Drawing on peer-reviewed studies published between 2020 and 2024 across the Scopus databases, the review integrates quantitative mapping and qualitative synthesis to identify emerging research patterns, thematic concentrations, and methodological trends. The findings reveal a marked surge in scholarly engagement since 2020, reflecting growing recognition of AI as both a strategic and operational driver of competitiveness in SMEs. Three dominant thematic clusters emerge: (1) AI-driven innovation, encompassing applications such as machine learning, predictive analytics, automation, and process optimization; (2) entrepreneurial decision-making, highlighting how AI supports opportunity recognition, strategic planning, and market sensing; and (3) adoption barriers and enablers, focusing on organizational readiness, digital infrastructure, human capital, and financial capability. While the field demonstrates expanding theoretical and practical interest, significant gaps remain regarding methodological diversity, contextual variation across developing economies, and the integration of socio-ethical considerations in AI adoption. The study contributes by consolidating fragmented knowledge, mapping the intellectual structure of the domain, and offering actionable implications for policymakers, practitioners, and scholars seeking to leverage AI to foster innovation, digital resilience, and sustainable growth in SMEs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.