Design and Evaluation of an Automated Document Management System for Corporate Offices

Thesis Abstract

In the contemporary corporate environment, efficient document management is pivotal to enhancing organizational productivity, compliance, and operational transparency; however, many offices continue to rely on manual or semi-automated systems that are often plagued by inefficiencies, security vulnerabilities, and difficulties in retrieving and sharing information. This study aims to design, implement, and evaluate an automated document management system (DMS) tailored for the needs of corporate offices to address these existing inefficiencies and facilitate seamless document workflows. The specific objectives include assessing current document management practices, identifying system requirements, developing a prototype DMS integrating features such as version control, access rights, and intelligent search, and evaluating its effectiveness in real-world office settings. A mixed-methods research design was adopted, combining qualitative and quantitative approaches to ensure comprehensive analysis. The qualitative component involved semi-structured interviews with 30 office managers and administrative staff across five corporate organizations to explore existing challenges and system requirements. Quantitative data were gathered via structured surveys administered to 150 employees from the same organizations, providing broad insights into user needs, system acceptance, and anticipated benefits. The prototype DMS was developed using agile software development methodologies, incorporating cloud-based infrastructure, metadata tagging, OCR (Optical Character Recognition), and role-based access controls. For evaluation, a quasi-experimental design was employed, with a sample of 60 employees divided into control and experimental groups; the experimental group used the new DMS over a period of three months, while the control group continued with manual processes. Data collection instruments included interview guides, structured questionnaires, system usability scales, and system performance logs. Validity and reliability of survey instruments were ensured through pilot testing, Cronbach’s alpha (0.87), and expert validation. Data analysis involved descriptive statistics, thematic analysis for qualitative insights, and inferential statistics including paired t-tests and regression analysis to evaluate system impact. Specifically, multiple regression was used to determine the relationship between system usage and productivity indicators, while thematic analysis was applied to qualitative interview transcripts to identify recurrent themes related to user satisfaction, system usability, and operational barriers. Expected findings suggest that the implementation of the automated DMS significantly improves document retrieval times, reduces errors, enhances security, and increases user satisfaction compared to manual practices. Quantitative analysis is anticipated to reveal statistically significant correlations between system adoption and improvements in document handling efficiency (p < 0.01), with regression models confirming system usability as a predictor of increased productivity. Qualitative insights are expected to highlight critical factors influencing system acceptance, including ease of use, training needs, and organizational change management. This study contributes to knowledge by demonstrating the practical application of systems design principles rooted in the Activity Theory and the Technology Acceptance Model (TAM) within the context of corporate document workflows. It offers a replicable framework for developing tailored DMS solutions in similar organizational settings, filling a notable gap in existing literature that predominantly focuses on generic or industry-specific frameworks without comprehensive evaluation in actual office environments. The main conclusion underscores the effectiveness of automated DMS in transforming traditional document management practices, thereby advocating for wider adoption in corporate offices. Recommendations include strategic training programs to maximize user acceptance, ongoing system evaluations to ensure adaptability, and policy developments to embed automated systems into organizational workflows. Future research avenues suggested include longitudinal studies to assess long-term impacts and the integration of artificial intelligence for enhanced document analysis and decision-making support.