Dr.Nicholas George
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Research

Research

Who shares what they know?

I saw it in every field, so I made it my doctorate. The MBA at the University of Bolton (with The Bolton Award) had already taught me to test theory against real work. The doctorate, completed at Manchester Metropolitan University, studied knowledge-sharing behaviour: how people perceive sharing, what they intend, what they actually do, and what makes them share or hold back. In my reading, those patterns are what AI now runs on. Models are trained on what people chose to share, use it to answer, and pass it on to whoever asks. That is where AI's influence on organisations comes from, and also where it can quietly thin out the human expertise it draws on. Neither can be judged from a vendor's slide.

Publications

Conference papers

  • “Knowledge Sharing Patterns: German and UK”, IFKAD 2022, Lugano
  • IFKAD 2024, Madrid
  • “Robotic Process Automation”, BAM 2022
Read the research scenarios (illustrations, not findings)

Illustrative scenario

A team assistant answers faster than colleagues

Claim. A company introduces an AI assistant and says that staff will find answers faster.

System behaviour. The assistant answers at once and its replies look polished, although their substance varies.

Human response. The question is whether colleagues are still seen as experts when a machine answers first, and whether peers keep giving each other feedback or stop.

Business consequence. If peer exchange fades, the knowledge that lives between people may thin out, even while the answers get quicker.

Decision. Before a wider rollout, run a bounded pilot with interviews alongside usage figures, and decide whether to extend, redesign or wait.

The thesis behind this lens studied online communities, not AI. The scenario applies the lens to a new setting; it does not show that AI behaves the same way.

Illustrative scenario

One AI rollout, two countries

Claim. A company plans to roll out the same AI tool to its British and German teams in the same way.

System behaviour. The tool behaves identically for every user.

Human response. The thesis sample found that UK and German participants described different styles of networking and feedback. The scenario asks whether the same rollout would then feel different in each country.

Business consequence. A single rollout plan could suit one team better than the other, and the company would learn this only after the fact.

Decision. Test the question with new interviews in both countries, and decide whether to deploy one plan, adapt it per team or wait.

The finding comes from a study of knowledge-sharing behaviour among digital entrepreneurs in UK and German low-code and no-code communities. It is not evidence about AI deployments.

The work, field by field

  1. AI Models

    Local models, one of them my own fine-tune, and cloud models beside them, each kept for a job it earned in testing.

  2. Benchmarks & Testing

    Measured numbers with their date, quantisation and setup beside them, the poor results included.

  3. Business Frameworks

    Four finished manuscripts with 134 compact frameworks, from an MBA and nearly two decades of work.

  4. EU AI Policy

    A registered expert who has drafted an independent review of the EU AI Act transparency guidelines.

  5. Knowledge & Decisions

    A completed PhD on knowledge sharing, and a toolkit for recording better decisions.

Illustration. Knowledge goes in, is measured and checked, and leaves as a decision.