A Systematic Review of Knowledge Management in Community-based Social Service Organizations

Abstract

This paper presents a systematic literature review of knowledge management (KM) challenges and design implications within community-based social service organizations (CSSOs), using the Knowledge Management Cycle (KMC) model proposed by Evans et al. [34] as an analytical framework. Through an analysis of 25 HCI and CSCW articles from 2007 to 2025, this study identifies KM challenges across each KMC phase–Identify/Create, Store, Share, Use, Learn, and Improve–and their ripple effects on organizational effectiveness. Key challenges include misalignment between mandated data practices and organizational goals, difficulties in capturing tacit and relational knowledge, logical, longitudinal, and locational data fragmentation, and systemic issues such as organizational silos, difficulties in translating data into actionable insights, and learn from prior experiences. The paper synthesizes design implications that can address such interconnected challenges, such as mission-aligned KM systems, effective capture and sharing of tacit knowledge, centralized data repositories, cross-organizational knowledge flow mechanisms, and frontline worker discretion support. Additionally, the paper introduces the human-AI teaming framework, advocating AI as a collaborative partner rather than an automation tool, highlighting its potential to ethically and effectively enhance KM practices within CSSOs. The study offers a synthesis of past research in this domain and suggests research agenda for the ethical integration of AI in high-stakes social service contexts, aiming to augment rather than replace human judgment and expertise.