The Structure and Dynamics of Groups in Open Source Software Development: A Computational Social Science Approach to Understanding Online Collaboration

  • This dissertation examines Free/Libre Open Source Software (FLOSS) development groups through three interconnected studies, each applying computational social science methods to understand different aspects of online collaboration. The first study analyzes group interactions via pull requests, identifying five organizational structures ranging from hierarchical to collaboratively governed networks. This typology moves beyond the traditional "bazaar-cathedral" dichotomy and reveals how group structure impacts outcomes such as popularity, stability, and productivity. The second study employs an agent-based model informed by Affect Control Theory to explore how cultural dynamics shape roles, status, power distribution, and gender biases within FLOSS communities. Findings illustrate the interplay between cultural norms and social structures, highlighting pathways toward gender equality through cultural norm shifts. The third study investigates macro-level project dynamics, examining how repository fitness, preferential attachment, and aging influence project popularity. It compares the meritocratic nature of scientific research and FLOSS development, employing generative probabilistic models based on stochastic processes to understand the mechanisms driving popularity. Together, these studies demonstrate how platform design, cultural norms, and social structures collectively shape FLOSS projects, advancing our understanding of digital collaborative systems.

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Publishing Institution:IRC-Library, Information Resource Center der Constructor University
Granting Institution:Constructor Univ.
Author:Nikolas Zöller
Referee:Jan Lorenz, Tobias Schröder, Marc-Thorsten Hütt
Advisor:Jan Lorenz
Persistent Identifier (URN):urn:nbn:de:gbv:579-opus-1013021
Document Type:PhD Thesis
Language:English
Date of Successful Oral Defense:2024/07/16
Date of First Publication:2025/04/24
PhD Degree:Data Engineering
Academic Department:School of Business, Social and Decision Sciences
Call No:2024/24

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