A Knowledge-Driven Framework for Digital Office Workflows and Collaboration

Thesis Abstract

The rapid digitization of office environments has elevated the need for integrated knowledge-driven systems that unify workflows, collaboration, and decision-making across organizational silos. Despite advances in digital tools, many offices struggle with fragmented information flows, redundant processes, and inconsistent collaboration outcomes, leading to reduced productivity and suboptimal knowledge reuse. This study develops a knowledge-driven framework that orchestrates digital office workflows and collaboration by aligning data, processes, and human expertise through explicit ontologies, context-aware rule sets, and collaborative analytics. The aim is to design, validate, and evaluate a framework that enhances workflow efficiency, accelerates knowledge sharing, and improves decision quality in digital office environments. The specific objectives are (1) to conceptualize a Knowledge-Driven Office Framework (KDOF) integrating ontologies, process mining, and collaborative filtering to support intelligent workflow routing; (2) to operationalize a functional architecture comprising a knowledge base, workflow engine, and collaborative workspace with role-based access and provenance tracking; (3) to empirically assess the framework’s impact on workflow cycle time, error rate, and user satisfaction; (4) to identify organizational and technical determinants of adoption and sustained use; and (5) to propose governance and ethics guidelines for knowledge stewardship in digital offices. The study adopts a mixed-methods design underpinned by the Structuration Theory and the Knowledge Management lifecycle. In the quantitative strand, a quasi-experimental design will compare two groups across a six-month implementation in three mid-sized organizations (n = 180 employees) using a pretest–posttest approach. Data will be collected with standardized instruments including the Workflow Efficiency Scale (WES), the Knowledge Sharing Climate Survey (KSCS), and the System Usability Scale (SUS). Inferential analysis will employ structural equation modeling (SEM) to test the hypothesized relationships among knowledge integration, workflow performance, and collaboration satisfaction, complemented by regression analyses to identify predictors of adoption. In the qualitative strand, semi-structured interviews (n = 36 participants) and focus groups (n = 6 groups) will explore perceived barriers, trust in automated routing, and contextual facilitators, with thematic analysis conducted in accordance with Braun and Clarke’s approach. Additionally, process-mining techniques will be applied to system logs to characterize actual workflow patterns, bottlenecks, and knowledge reuse rates. The expected findings include (a) evidence that the KDOF reduces average workflow cycle time by 22–28% and lowers error incidence in document routing by 15–20%, (b) enhanced perceived collaboration quality and knowledge sharing climate, (c) positive correlations between explicit knowledge representations and task throughput, moderated by user trust and perceived usefulness, and (d) differentiated adoption trajectories across functional roles with actionable determinants such as perceived data quality, provenance transparency, and governance clarity. The study will also reveal critical design trade-offs between automated decision support and human judgment in knowledge-intensive tasks. Contributions to knowledge comprise (i) the operationalization of a novel Knowledge-Driven Office Framework that integrates ontological knowledge representation, process mining, and collaborative analytics within a unified architecture; (ii) empirically validated links between knowledge integration and measurable office performance outcomes; (iii) a theory-informed understanding of adoption dynamics for knowledge-driven office technologies incorporating Structuration Theory and knowledge governance, with practical implications for change management; and (iv) a set of design principles and governance recommendations for ethical data use, provenance, privacy, and trust in digital office ecosystems. The study concludes that a knowledge-driven approach to digital office workflows and collaboration substantially enhances efficiency and knowledge reuse when coupled with transparent governance, user-centered design, and robust provenance mechanisms. Recommendations include (a) adopting modular, interoperable ontologies aligned with core office processes; (b) implementing lightweight process mining dashboards for ongoing performance feedback; (c) establishing clear data governance and ethics policies to sustain trust and compliance; and (d) fostering continuous learning communities to sustain collaboration quality and knowledge circulation over time.