The Knowledge Management Handbook: Collaboration and Social Networking Review
The Knowledge Management Handbook edited by Jay Liebowitz focuses on how collaboration platforms and social networking reshape KM. It treats sharing as a design problem: align tools, incentives, and governance so knowledge flows where work happens. The message is practical: KM succeeds when communities, workflows, and analytics reinforce each other.
Overview
Chapters cover enterprise social tools, communities of practice, expertise location, collaboration analytics, governance, and security. Case studies show adoption patterns, failure modes, and metrics tied to business outcomes rather than content volume.
Summary
The handbook frames KM as a service portfolio: communities, social Q&A, microblogging, wikis, and expert finders supported by roles and lightweight processes. It emphasizes seeding behaviors, curating signals, and closing loops via lessons learned and after-action reviews. Measurement centers on reuse, time-to-competence, and decision speed.
Authors
Jay Liebowitz curates contributions from academics and practitioners. The tone balances frameworks and field evidence, with attention to adoption and change management.
Key Themes
Communities as engines of tacit transfer; social platforms as context-rich KM; incentives and recognition as fuel; analytics to surface experts and emerging topics; governance to keep quality and security intact.
Strengths and Weaknesses
Strengths: actionable guidance for social KM, varied cases, and metric templates. Weaknesses: uneven depth across chapters and lighter coverage of modern LLM-enabled workflows. Use it as a playbook for collaboration-centered KM.
Target Audience
KM leaders, HR/L&D, product and engineering managers, and digital workplace owners implementing social collaboration at scale.
Favorite Ideas
Expertise location via network signals; community stewardship as a formal role; microlearning loops embedded in collaboration tools.
Takeaways
Design KM around communities and social workflows. Fund stewards, measure reuse and cycle time, and use analytics to guide curation. If collaboration improves decisions, KM is working.
