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Social Networking: What Does It Mean for Knowledge Management?

APQC·
5 min read

Based on APQC's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.

TL;DR

Expect knowledge creation to come from a small minority of active users, while most participants will primarily consume information.

Briefing

Social networking’s biggest knowledge-management implication isn’t that everyone will contribute—it’s that a small, highly active minority will generate most of the usable content, while the majority mostly consumes. Facebook illustrates the imbalance starkly: roughly 90% of content is created by about 20% of users. Twitter is even more lopsided, with only about one out of five registered users ever tweeting. For knowledge management (KM), that means communities of practice and other knowledge-sharing efforts will likely still depend on a “tip of the iceberg” group of prolific contributors, even as the underlying platforms change. The human behavior doesn’t reset just because the code does.

At the same time, social networking brings a countervailing benefit: more people are creating content than ever before. That shift matters because KM has long struggled with getting knowledge out of silos and into shared, searchable channels. The practical challenge becomes how organizations harness that content flow without drowning in noise or losing control of quality.

A set of guidance points from an APQC advanced working group frames the approach around four themes. First, organizations shouldn’t assume they need to build another “Facebook” internally. People already have a social space; what they need is a clear organizational mission and a way to apply social networking functionality to work-relevant outcomes. In other words, the value comes from aligning social tools to the organization’s purpose, not from replicating social platforms for their own sake.

Second, scale matters, but not in the simplistic way many expect. Public social networks include enormous audiences—Facebook alone is cited at about 400 million users, and Twitter at about 15 million—while the broader internet reaches roughly 1.3 billion people. Even a small participation rate can yield large numbers of active participants. Still, internal rollouts face different math: with only 5,000 employees, participation will be lower than on public platforms, so organizations should plan for that reality rather than panic.

Third, change management remains essential. Migration to social tools still requires communication and adoption tactics; organizations can’t rely on goodwill alone. Trust but verify: if employees aren’t genuinely well intentioned, social networking efforts shouldn’t proceed, but even with good intentions, organizations should monitor outcomes and provide boundaries.

Finally, KM measurement needs a different posture. Traditional KM guidance emphasizes identifying what matters and measuring it. Here, the recommendation is to watch and discover first—then form hypotheses, test what works, and use analytics to mine what’s happening. Because tools and technology will keep changing, the guidance also stresses building for change and keeping implementations agnostic so the KM approach survives platform shifts.

Cornell Notes

Social networking affects knowledge management less by changing human behavior and more by changing the volume and distribution of contributions. Facebook and Twitter both show heavy participation skew: a small minority creates most content, while most users mainly read. The opportunity for KM is that content creation is higher than ever, enabling faster sharing and discovery of knowledge. The practical guidance is to align social functionality to an organization’s mission, plan for participation rates that differ from public networks, and keep change management and monitoring in place. Measurement should start with observation and discovery, then evolve into hypotheses and analytics as patterns emerge, while staying agnostic to future tool changes.

Why does participation skew matter for knowledge management on social platforms?

Facebook is cited as having about 90% of content created by roughly 20% of users, while Twitter is cited as having only about one out of five registered users ever tweeting. That means KM efforts will likely rely on a small “prolific” group to generate most knowledge artifacts, with the broader population acting more as consumers. This mirrors dynamics seen in communities of practice: a visible minority drives discussion and content, while many others read, learn, and occasionally contribute.

What’s the recommended stance on building an internal “Facebook” for employees?

The guidance is that organizations don’t need another Facebook-like platform because employees already have one. Instead, the mission should be clear: apply social networking functionality to organizational goals and work outcomes. The key is not recreating social life, but channeling social behaviors toward knowledge-sharing needs.

How should organizations think about scale and participation rates?

Public networks operate at massive scale—Facebook is cited at about 400 million users, Twitter at about 15 million, and the internet at roughly 1.3 billion people. Even a small percentage of participation can still produce large active communities. For internal deployments, the math changes: with 5,000 employees, participation will be much lower than on public sites. The recommendation is to anticipate that difference and design adoption and engagement accordingly.

Why does change management still matter when adopting social networking tools?

Adoption doesn’t happen automatically. Even if employees are well intentioned, organizations must communicate, migrate, and encourage use—similar to earlier change efforts. The guidance uses “trust but verify”: proceed only if employees are expected to act responsibly, but still monitor activity to ensure nothing harmful emerges and provide guidelines for appropriate behavior.

How should KM measurement approach social networking differently from traditional methods?

Traditional KM measurement emphasizes identifying what matters and measuring it. With social networking being new and fast-moving, the guidance is to watch and discover first, then develop hypotheses about what works. Analytics should support that learning loop—mining information as patterns emerge—rather than assuming upfront metrics will capture value.

What does “build for change” mean in this context?

Because social tools and underlying technology will keep changing, KM implementations should be agnostic—designed so that the approach can adapt when platforms evolve. The goal is to avoid locking the organization into a specific toolset that may not last.

Review Questions

  1. How do the cited participation ratios on Facebook and Twitter change expectations for who will create knowledge artifacts in an organization?
  2. What practical steps follow from “trust but verify” when deploying social networking for KM?
  3. Why does the guidance recommend “watch and discover” before settling on measurement priorities?

Key Points

  1. 1

    Expect knowledge creation to come from a small minority of active users, while most participants will primarily consume information.

  2. 2

    Align social networking functionality to the organization’s mission rather than trying to replicate public social platforms internally.

  3. 3

    Plan for participation rates that differ from public networks; internal rollouts with thousands of employees will naturally show lower participation.

  4. 4

    Treat social networking adoption as a change-management effort, using communication and migration tactics to drive usage.

  5. 5

    Use monitoring and guidelines to manage risk, applying “trust but verify” rather than assuming good outcomes automatically.

  6. 6

    Start measurement with observation and discovery, then move to hypotheses and analytics as patterns emerge.

  7. 7

    Design KM approaches to be agnostic to tool and technology changes so the strategy survives platform shifts.

Highlights

Facebook’s cited pattern—about 90% of content created by 20% of users—implies KM will depend on a small group of prolific contributors.
Twitter’s cited skew—only about one out of five registered users ever tweeting—suggests most users will read more than they write.
Organizations don’t need another internal Facebook; employees already have social spaces, so the mission must determine how social tools are applied to work.
“Watch and discover” replaces immediate metric-setting: analytics should support a learning loop of hypotheses and testing.
Because tools will change, KM implementations should be agnostic and built for ongoing platform evolution.

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