Beyond resource constraints: how Ibero-American SMEs leverage AI for competitive advantage through strategic capability development

Martin Sposato & Eduardo Carlos Dittmar

Journal of Strategy and Management2026https://doi.org/10.1108/jsma-08-2025-0303article
AJG 1ABDC B
Weight
0.50

What the paper says

Purpose This research formulates an integrative framework bridging established strategic theories and artificial intelligence (AI) adoption in Ibero-American small and medium-sized enterprises (SMEs), addressing how resource-constrained organizations navigate digital transformation challenges. Design/methodology/approach Employing a structured narrative literature review following Torraco (2016) and Juntunen and Lehenkari (2021), this study synthesizes 72 articles from multiple databases (2015–2024). The analysis integrates resource-based view (RBV), dynamic capabilities theory and institutional theory through thematic synthesis and systematic coding procedures. Findings Four interrelated drivers enable SMEs to overcome resource constraints: strategic synchronization, leadership commitment, technology sensing and institutional bridging. These drivers result from iterative thematic synthesis, demonstrating how organizational antecedents interact with contextual moderators through implementation processes to generate performance outcomes. Originality/value The framework extends strategic management theories to emerging market contexts, offering eight testable propositions linking capability development to AI-driven performance. For practitioners, findings highlight how SME leaders can align AI initiatives with strategic capability growth to improve competitiveness despite resource limitations.

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https://doi.org/https://doi.org/10.1108/jsma-08-2025-0303

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@article{martin2026,
  title        = {{Beyond resource constraints: how Ibero-American SMEs leverage AI for competitive advantage through strategic capability development}},
  author       = {Martin Sposato & Eduardo Carlos Dittmar},
  journal      = {Journal of Strategy and Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jsma-08-2025-0303},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.