Abstract

A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge collaborative network. Four social network analysis indexes were created to identify the key opinion leader nodes in the network using the entropy weight and TOPSIS method. Further, three degradation modes were designed for (1) the collaborative behavior of opinion leaders, (2) main knowledge dissemination behavior, and (3) main knowledge contribution behavior. Regarding the degradation model of the collaborative behavior of opinion leaders, we considered the propagation characteristics of opinion leaders to other nodes, and we created a susceptible–infected–removed (SIR) propagation model of the influence of opinion leaders’ behaviors. Finally, based on empirical data from the Local Motors open-source vehicle design community, a dynamic robustness analysis experiment was carried out. The results showed that the robustness of our constructed network varied for different degradation modes: the degradation of the opinion leaders’ collaborative behavior had the lowest robustness; this was followed by the main knowledge dissemination behavior and the main knowledge contribution behavior; the degradation of random behavior had the highest robustness. Our method revealed the influence of the degradation of collaborative behavior of different types of nodes on the robustness of the network. This could be used to formulate the management strategy of the open-source design community, thus promoting the stable development of OSPs.

Highlights

  • In contrast to a traditional product design process, the open-source design (OSD)community is spontaneously organized by diverse community members according to their interests and needs

  • There are many cases where the open-source community (OSC) has declined or failed due to the loss of a large number of members or the degradation of behavior [15,16]. It is of great practical significance for the stable development of OSCs and open-source projects (OSPs) to conduct research on the robustness of knowledge collaborative network (KCN) in the face of the negative impacts caused by the negative behaviors of OSC opinion leaders

  • 0.118992 t df Through further observation of the change of the index value we find that when t = 5, the relative size of network connectivity and weighted efficiency under the degradation mode of opinion leaders’ collaborative behavior (CN) decreased by 80%, while the index value under the degradation modes of main knowledge dissemination behavior (BS) and main knowledge contribution decreased by only 40%

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Summary

Introduction

In contrast to a traditional product design process, the open-source design (OSD). community is spontaneously organized by diverse community members according to their interests and needs. Similar to other forms of networks (e.g., social network, software network, business network, etc.), important nodes in the OSD community network—known as “opinion leaders”—dominate knowledge sharing, information dissemination, public opinion orientation, behavior, and decision-making guidance [4,5]. These opinion leaders transmit their views, ideas, models, and other information to other designers via their online community’s information communication channels, and this information is radiated to the whole network. It is of great practical significance for the stable development of OSCs and open-source projects (OSPs) to conduct research on the robustness of KCNs in the face of the negative impacts caused by the negative behaviors of OSC opinion leaders

Opinion Leader Identification
The Propagation of Opinion Leader Behavior
Complex Network Robustness
Semantic-Based Weight Calculation
KCN Structure Characteristics
Opinion Leader Identification Based on Entropy Weight and TOPSIS
Analysis of Identification Index
Entropy Weight TOPSIS
Opinion Leader Identification Example
SIR Propagation Model of Opinion Leaders’ Behavior Influence
Robustness Evaluation Index
Relative Size of Network Connectivity S
Relative Size of Weighted Efficiency H
Degradation Mode Design of Knowledge Collaboration Behavior
Simulation Analysis on Dynamic Robustness of KCN
Findings
Conclusions

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