Unified Adaptive Agency Theory: How the Coupling of Structural and Processual Subsystems Drives Organizational Agency

Read full text
Authors: Guilin Yang
Cited by: 1
References: 23

Abstract

Abstract The internal mechanism of proactive evolution under uncertainty remains a core puzzle in organizational research. Existing theories often overemphasize structure while neglecting agency. Others focus on cognition but detach it from the entity. Consequently, they struggle to explain how micro-level mentality transforms into macro-level adaptation. To bridge this gap, this paper proposes the Unified Adaptive Agent Theory (UAAT). We view the organization as an agent driven by the dynamic coupling of a structural subsystem (Resources-Capabilities-Intentions, RCI) and a process subsystem (Cognition-Imagination-Learning, CIL). Based on text analysis of annual reports from Chinese A-share listed companies and large-sample empirical tests, we present several findings. First, higher environmental uncertainty leads firms to strengthen the structural fit between internal resources and strategic intentions. This demonstrates a distinct "internal anchoring" rationality. Second, while the process subsystem has limited direct contribution to value creation, cognitive levels determine adjustment sensitivity. Meanwhile, imagination significantly guides strategic intent. Third, structural synergy contributes significantly more to long-term firm value than process synergy. This confirms that physical embodiment is the foundation of organizational adaptability. These results suggest that proactive evolution is not merely strategic conceptualization. Rather, it is the dynamic result of mental processes continuously reshaping physical structures. This study provides a micro-macro integration mechanism for organizational agencies. It also re-establishes the theoretical status of the "organization" as the core unit of analysis for economic and social evolution.

Fields of Study & Subjects

Chaos Complexity and Education Innovation Sustainability Human-Machine Systems Innovation and Knowledge Management

Publication details

Journal / source
Research Square
Publisher
Springer Science and Business Media LLC
Publication date
1 July 2026
Document type
Preprint