Impact of Digital Transformation on Executive Assistants’ Productivity and Roles

Thesis Abstract

The rapid integration of digital technologies into organizational processes has transformed the work of executive assistants (EAs), redefining their roles from traditional administrative support to strategic partners in governance and information management. This study addresses the problem of how digital transformation (DT) influences EA productivity and role delineation, with attention to variations across organizational size, sector, and level of technology maturity. The aim is to examine the relationship between DT initiatives and EA productivity outcomes, identify changes in role scope, and elucidate the mediating mechanisms through which technology shapes daily EA practice. Specific objectives include (1) assessing changes in task efficiency, accuracy, and response times associated with DT adoption; (2) mapping shifts in role responsibilities, decision- making authority, and strategic involvement; (3) identifying technological competencies and training needs that most strongly predict productivity gains; (4) evaluating organizational and cultural factors that facilitate or hinder DT-enabled performance; and (5) proposing a framework linking DT capabilities to EA effectiveness and career progression. The study adopts a cross-sectional, mixed-methods design conducted in multiple organizations across the financial services, technology, and professional services sectors. Population comprises employed EAs with at least two years of experience. A stratified random sample of 320 EAs will be drawn, with 240 completing a structured survey and 40 participating in in-depth interviews to enrich context-specific insights. Data collection instruments include a validated questionnaire measuring digital maturity, technology usage patterns (e.g., automation, collaboration platforms, AI-assisted scheduling), task-technology fit, psychological empowerment, and productivity indicators (e.g., task completion rate, error rate, meeting preparation turnaround). Semi-structured interview guides will explore shifts in role responsibilities, perceived autonomy, and strategic engagement. Validity and reliability will be ensured through pilot testing, test-retest reliability analyses, and triangulation across survey data, interview transcripts, and organizational performance metrics. Quantitative data will be analyzed using multiple regression to identify predictors of productivity, mediation analysis to test the role of task-technology fit and perceived empowerment, and ANOVA to compare differences across organizational size and sector. Qualitative data will be analyzed thematically using a reflexive approach aligned with Braun and Clarke (2006), with coding validated via member checking and intercoder agreement. Expected findings anticipate that higher levels of digital maturity and sophisticated automation correlate positively with EA productivity, manifesting as reduced task completion times, lower error rates, and enhanced responsiveness to senior executives. The study also expects to find an expanded EA role, with increased involvement in information governance, strategic scheduling, and cross-functional coordination, moderated by organizational culture and leadership support. Mediating mechanisms likely include perceived task-technology fit, psychological empowerment, and access to continuous learning opportunities. Sectoral and size-related variations are anticipated, with larger organizations and technology-intensive firms exhibiting more pronounced role expansion and productivity gains, contingent on effective change management and training programs. Contribution to knowledge includes (a) empirical evidence linking digital transformation maturity to specific productivity metrics and role evolution for executive assistants; (b) a validated conceptual framework integrating technology capabilities, individual factors, and organizational contexts to explain EA effectiveness; and (c) practical implications for human resource development, information governance, and leadership practices in enabling DT-enabled performance. The study informs theory by extending the literature on digital work and boundary-spanning roles to the subordinate support level, and it contributes methodologically through a robust mixed-methods approach and a multi-industry comparative design. The main conclusion is that digital transformation enhances EA productivity and expands role scope when organizations align technology investments with targeted training, clear governance structures, and supportive leadership culture. Recommendations include (1) developing structured DT training curricula emphasizing automation literacy and data governance for EAs; (2) instituting governance models that delineate decision-rights and collaboration channels between EAs and executives; (3) investing in adaptive performance metrics that capture both routine efficiency and strategic contribution; and (4) fostering organizational learning partnerships to sustain ongoing skill development and technology adoption.