A Knowledge-Based Framework for Digital Office Technology Adoption and Productivity

Thesis Abstract

The rapid digitization of office environments has intensified the need for a coherent knowledge-based approach to adopting digital office technologies that directly enhances organizational productivity. Despite widespread availability of tools such as collaborative platforms, AI-assisted document management, and automation workflows, organizations struggle to convert technology investments into measurable performance gains due to fragmented knowledge, insufficient alignment with work processes, and insufficient transfer of tacit know-how to explicit, reusable rules. This study aims to develop a knowledge-based framework that links digital office technology adoption to productivity outcomes, by integrating human capital, organizational routines, and technological affordances into a coherent model. The objectives are (1) to identify the key knowledge assets and organizational routines that influence successful adoption of digital office technologies; (2) to develop a formal knowledge-based framework that prescribes process-embedded rules for technology selection, customization, and workplace integration; (3) to empirically test the framework across diverse office settings to determine its explanatory power for productivity improvements; and (4) to provide actionable guidelines for practitioners to operationalize knowledge-based governance in technology rollout. The study adopts a sequential mixed-methods design grounded in the Knowledge-Based View and Activity Theory. In the quantitative phase, a cross-sectional survey will be administered to 420 office professionals across five organizations implementing digital office suites, document management systems, and AI-assisted collaboration tools. The instrument will measure constructs including knowledge assets (tacit and explicit), organizational routines (standardization, improvisation, and redundancy), technology characteristics (usability, interoperability, and automation level), implementation climate, and productivity indicators (task completion rate, cycle time, and error rate). Reliability and validity will be established through Cronbach’s alpha, confirmatory factor analysis, and convergent validity checks. Structural equation modeling (SEM) will be employed to test the hypothesized relationships in the knowledge-based framework, with model comparison against competing theories such as the Technology-Organization-Environment (TOE) framework. In the qualitative phase, 40 semi-structured interviews with knowledge managers, IT leaders, and end-users will be conducted to capture contextual nuances, complemented by 20 process observations to map routine enactment. Thematic analysis will identify recurring patterns of knowledge flow, barriers to adoption, and enablers of productive use, which will be integrated into the framework through a mixed-methods synthesis. Expected findings include (a) a validated knowledge-based framework specifying how explicit knowledge artifacts (guidelines, decision trees, and ontologies) and tacit knowledge (expertise, community of practice) interact with office routines to influence adoption speed, utilization depth, and productivity outcomes; (b) evidence that higher-quality knowledge governance mediates the relationship between technology complexity and productivity gains; (c) identification of critical moderating factors such as organizational learning culture, leadership support, and change fatigue; and (d) quantified effect sizes demonstrating the impact of knowledge-driven adoption on task efficiency (average reduction in cycle time by 18–25%), accuracy (error rate reduction by 12–20%), and perceived productivity (employee self-report improvement by 15–22%). The study contributes to knowledge by operationalizing a novel knowledge-based framework that integrates information economics, human-capital orientated theory, and organizational routines to explain digital office technology adoption outcomes. It advances methodological practice by combining SEM with thematic analysis in a coherent mixed-methods design and by providing a replicable measurement instrument for knowledge assets and routines in IT-enabled offices. The practical implications include a set of governance artifacts—knowledge schema, rule-based workflow templates, and adoption playbooks—that organizations can deploy to optimize technology investments, reduce acceptance resistance, and sustain productivity improvements. The main conclusion is that productivity gains from digital office technologies hinge less on feature sets and more on deliberate, codified knowledge management that aligns technology capabilities with everyday work routines. Recommendations emphasize establishing a centralized knowledge repository, cultivating communities of practice for continuous learning, and embedding knowledge governance into change management processes to sustain differential productivity effects across organizational contexts.