THE ROLE OF STRATEGIC COACHING IN THE SYSTEM OF STRATEGIC MANAGEMENT AND CORPORATE GOVERNANCE
Abstract
The article conceptualizes strategic coaching as a missing link between strategic management and corporate governance in high-uncertainty environments. Adopting a systems perspective, it integrates three loops of strategic learning—operational, strategic, and reflective — and positions coaching across different stages of a company’s life cycle: the full strategy cycle: Pre-Start, Start-Up, Scale-Up, Institutional development; and for key entrepreneurial roles: Conceptual Designer, Founder, CEO, Governor. The paper advances a role-selection logic for the strategic coach (entrepreneur-as-coach, CEO-as-coach, peer/inside-team coach, external executive coach), a responsibility RACI matrix connected to the company life cycle stages, and a measurement suite combining leading and lagging indicators (quality of strategic conversations, decision velocity and quality, hypothesis validation ratio, team maturity). Governance implications are developed by allocating the board’s role as “architect of coaching,” balancing control and learning through policies, KPIs, board-CEO development contracts, and psychological safety safeguards. The framework couples dynamic capabilities and organizational learning with evidence on coaching effectiveness to derive testable propositions about performance pathways. Implementation guidance is provided, focusing on board oversight, ethics, and confidentiality, and integration with strategy management offices. The article contributes by (1) defining strategic coaching as a distinct governance-enabled capability, (2) mapping who should coach whom and when across the company life cycle, and (3) specifying coherent metrics and governance mechanisms to avoid common failure modes (role conflicts, instrumentalization, erosion of trust). Future research directions include multilevel designs to quantify the incremental impact of strategic coaching on decision quality, dynamic capabilities, and long-term performance, which include uncertainty, resource constraints, and institutional evolution, requiring robust learning architectures.
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