Structural Constraints as Moderators in the Ai–performance Relationship: Evidence from Smes in an Emerging Economy

Artificial intelligence (AI) adoption is increasingly recognized as a source of competitiveness for small and medium-sized enterprises (SMEs). Yet, prior research has primarily treated structural constraints such as financial scarcity, skill shortages, and institutional weaknesses as mere barriers, leaving their post-adoption impact underexplored, particularly in emerging economy contexts. This study empirically examines a relationship that has been theoretically acknowledged but rarely tested in such settings. Drawing on the Resource-Based View, Contingency Theory, and Institutional Theory, we propose a multidimensional framework explaining why AI adoption does not uniformly translate into performance gains but depends on firms’ financial capacity, technical competencies, and institutional environment. Using survey data from 280 Tunisian SMEs analyzed with partial least squares structural equation modeling (PLS-SEM), results confirm that AI adoption significantly improves financial and operational performance. Financial and technical strengths amplify these effects, whereas institutional conditions exert no significant moderating influence, suggesting that firms compensate for institutional weaknesses through adaptive and informal mechanisms. By reconceptualizing structural constraints as post-adoption moderators rather than pre-adoption barriers, this study advances understanding of contextual contingencies shaping AI outcomes and provides insights for managers and policymakers in resource-limited economies.