Toward a Theory of Hypermodern Agility: Rethinking Organizational Adaptation Mechanisms in the Era of Unstable Ecosystems

Abstract

In a hypermodern context characterized by technological acceleration, societal recomposition, and climate urgency, classical agility frameworks struggle to account for ecosystemic interdependencies and the plurality of temporal horizons. This article addresses the following research question: how can organizational agility be conceptualized to meet the singular challenges of the hypermodern era, marked by accelerated innovation cycles, market unpredictability, and a diversity of stakeholders? We advance the theory of “hypermodern agility,” grounded in three key latent variables: (i) the cognitive reconfiguration of decision-making routines, (ii) the plasticity of coordination structures, and (iii) the reflexivity of innovation ecosystems. The mixed-method design combines a systematic literature review (two thousand–2025), multi-sector case studies (36 interviews), and quantitative validation through PLS-SEM on a sample of 311 respondents. Qualitative findings reveal short-cycle sensemaking loops, modular organizational architectures, and inter-actor learning mechanisms. Quantitative results confirm a second-order construct of hypermodern agility explained by cognitive reconfiguration (β = 0.34; p < 0.001), coordination plasticity (β = 0.28; p < 0.001), and ecosystem reflexivity (β = 0.31; p < 0.001; R² = 0.62). Hypermodern agility enhances strategic performance (β = 0.42; R² = 0.49) and organizational sustainability (β = 0.35; R² = 0.45). Model robustness is supported (loadings ≥ 0.70; α = 0.82–0.91; AVE = 0.54–0.71; HTMT < 0.85; SRMR = 0.058; Q² > 0; bootstrapping 5,000). Environmental uncertainty strengthens the impact of agility on performance (β_mod = 0.12; p = 0.02). The theoretical contribution introduces the concept of “fractal temporality,” through which organizations articulate short-, medium-, and long-term horizons. Managerial implications concern algorithmic governance, adaptive steering, and ecosystem orchestration. Limitations relate primarily to sectoral scope and the absence of longitudinal tracking; comparative and experimental studies are proposed for future research.