Algorithmic Competitive Advantage under Institutional Constraint: Data, Orchestration, and Complementary Capabilities in Resource-Constrained Economies

Abstract

Artificial intelligence is altering the competitive advantage by lowering the cost of prediction. Strategic management, however, knows little how this transformation unfolds where the institutions that supply advantage's complements are themselves underdeveloped. Current study is an integrative conceptual review connecting the resource-based view, dynamic capabilities, the economics of artificial intelligence, research on human–AI complementarity, and the institution-based view. It advances two arguments. First, as general-purpose predictive technologies diffuse and converge across competitors, generic predictive capability is commoditized and competitive advantage relocates to complements that resist imitation. These include strategically embedded data assets, managerial judgment encoded in decision architectures, and the orchestration of human and algorithmic agents. Second, and centrally, the accumulation and appropriation of these complements are conditioned by the institutional and infrastructural environment. Data infrastructure, factor markets for specialized talent, property-rights and appropriability regimes, and information intermediaries each moderate a specific link in the pathway from algorithmic resources to advantage. The study therefore theorizes resource-constrained economies not as disadvantaged versions of advanced economies but as boundary conditions that reveal, with unusual clarity, the mechanisms linking algorithmic resources to advantage. There are theses that cheap artificial intelligence (AI) democratizes advantage and enables developing-economy firms to leapfrog and that it structurally widens the gap between rich and poor economies. Current study, however, argues that the binding constraint shifts from access to algorithms, which diffuse cheaply as general-purpose tools, to complementary capabilities whose accumulation is institutionally gated. Hence the two theses operate at different levels of analysis and reconcile once access and advantage are distinguished. Seven propositions specify these relationships in causal, testable form, defining the multidimensional construct of the resource-constrained institutional environment, operationalizing artificial-intelligence orchestration capability, and identifying the conditions under which firms either experience constrained value creation or convert institutional voids into proprietary capability and defensible position. The framework contributes to competitive-advantage theory by respecifying the scarcity structure that underpins advantage in the algorithmic firm; to the micro foundations program by relocating the explanatory locus from human action to human–algorithm interaction; and to the institution-based view by showing that institutions govern not only the adoption of artificial intelligence but the conversion of algorithmic resources into appropriated advantage.