RECONCILING DYNAMIC CAPABILITIES WITH INSTITUTIONAL FRICTION: A STRATEGIC HUMAN RESOURCE MANAGEMENT FRAMEWORK FOR COMPETITIVE ADVANTAGE IN SERVICE CONGLOMERATES

Abstract

Service conglomerates operating across aviation, hospitality, catering, and logistics face a strategic tension between the need for organizational agility and the persistence of bureaucratic and functional boundaries. This study develops and illustrates a structural model explaining how strategic human resource management (SHRM) capabilities can be translated into sustainable competitive advantage through cross-functional collaboration and work environment quality. The model integrates the resource-based view and dynamic capabilities perspective and examines transformational leadership efficacy, competency framework alignment, and motivational architecture as organizational inputs. Cross-functional collaboration and work environment quality are specified as mediating mechanisms, while sustainable competitive advantage is the focal outcome. To demonstrate the proposed empirical design, a simulated dataset of 312 service employees is analyzed using partial least squares structural equation modeling (PLS-SEM) with 5,000 bootstrap resamples. The simulated results indicate positive and statistically significant relationships from transformational leadership efficacy to cross-functional collaboration and work environment quality, from competency framework alignment to cross-functional collaboration, and from motivational architecture to work environment quality. Both mediators are positively associated with sustainable competitive advantage, while transformational leadership also retains a positive direct association with the outcome. The simulated indirect effects are consistent with complementary mediation. The article contributes to a theoretically integrated framework in which human capital is treated not merely as an administrative input but as a dynamic organizational resource that enables coordination, adaptation, and service differentiation. Because the numerical evidence is simulated, the findings should be interpreted as an analytical demonstration rather than empirical evidence.