Structure of Small World Innovation Network and Learning Performance

Abstract EN

This paper examines the differences of learning performance of 5 MNCs (multinational corporations) that filed the largest number of patents in China.

We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections.

To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality.

We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality.

A longer (shorter) distance between the knowledge of the individual and the reality denotes a lower (higher) knowledge level of that individual.

Individuals interact with and learn from each other within the small-world network.

By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs.

The network monopolization negatively impacts and network connectivity positively impacts learning performance.

Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.