A Dynamic Capability Framework for HR Digital Transformation success

Thesis Abstract

The rapid evolution of digital technologies and evolving workforce expectations have intensified the need for Human Resources (HR) functions to transition from traditional administrative roles to dynamic, capability-based capabilities that continuously sense, seize, and reconfigure resources to sustain competitive advantage. Despite extensive investments in HR digital tools, many organizations struggle to translate digital initiatives into sustained performance gains due to a lack of integrative theoretical frameworks that explain how organizational capabilities mediate digital transformation outcomes. This study develops a Dynamic Capability Framework for HR Digital Transformation (DCF-HRDT) to explain how HR functions build and leverage sensing, seizing, and reconfiguring capabilities to achieve successful digital transformation outcomes, including improved HR service delivery, employee experience, and strategic alignment. The aim is to theorize and empirically validate how dynamic capabilities within HR facilitate digital transformation success, and to identify the antecedents, moderating conditions, and performance consequences of these capabilities. The specific objectives are (1) to delineate the construct structure of HR dynamic capabilities—sensing capabilities for digital talent needs and market signals, seizing capabilities for digital opportunity mobilization, and reconfiguring capabilities for HR process and system transformation; (2) to examine the relationships between organizational learning, IT governance maturity, and HR dynamic capabilities; (3) to assess how internal alignment (leadership commitment, culture, and change readiness) and external ecosystem factors (vendor partnerships, regulatory environment) moderate the effect of HR dynamic capabilities on digital transformation outcomes; (4) to test a higher-order DCF-HRDT model predicting impact on HR service quality, employee engagement, and strategic HR alignment; and (5) to provide actionable guidance for HR leaders to methodological integrate digital tools with human-centric capabilities. A mixed-methods research design is employed. In the quantitative strand, a cross-sectional survey will be administered to HR leaders and line managers across 25 large- and mid-sized organizations in manufacturing, financial services, and technology sectors (n ? 600 respondents; response rate anticipated at ~60%). Measurement instruments will be adapted from validated scales for dynamic capabilities, IT governance maturity, organizational learning, change readiness, and digital HR outcomes, with confirmatory factor analysis (CFA) performed to establish construct validity. Structural equation modeling (SEM) will be used to test the hypothesized DCF-HRDT model, with additional multi-group analysis to explore sectoral differences. In the qualitative strand, 20 in-depth interviews with chief HR officers and senior HR analytics leads will be conducted to explore contextual conditions and to triangulate survey findings. Thematic analysis will be guided by the coding framework anchored in the sensing–seizing–reconfiguring taxonomy and guided by the theoretical lens of the dynamic capabilities framework (Teece, 2014) and the resource-based view when discussing internal resources. Data integration will employ a convergent parallel design, with triangulation informing model refinement. Expected findings include (a) empirical validation of a higher-order HR dynamic capability construct operating through sensing, seizing, and reconfiguring dimensions; (b) evidence that higher IT governance maturity and robust organizational learning significantly enhance HR dynamic capabilities; (c) identification of critical moderators—leadership commitment, change readiness, and external ecosystem engagement—that strengthen the link between HR dynamic capabilities and digital transformation outcomes; (d) demonstration that HR service quality, employee engagement, and strategic HR alignment mediate the relationship between HR dynamic capabilities and broader organizational performance metrics such as time-to-market for HR initiatives and talent retention rates. The study contributes theoretically by integrating Teece’s dynamic capabilities with HR-specific constructs, offering a parsimonious and testable framework for HR digital transformation. Practically, it delivers a diagnostic tool for HR leaders to assess capability gaps, design targeted interventions, and establish governance mechanisms that sustain digital transformation. The main conclusion is that sustained HR digital transformation success depends on cultivating a coherent, higher-order dynamic capability within HR that continuously senses external shifts, mobilizes opportunities, and reconfigures processes and systems, underpinned by strong governance, learning, and leadership. Recommendations include developing a standardized HR analytics maturity roadmap, instituting ongoing change-readiness programs, and forming strategic alliances with technology partners to bolster capability development.