Emergence of new project teams from open source software developer networks: Impact of prior collaboration ties

被引:159
|
作者
Hahn, Jungpil [1 ]
Moon, Jae Yun [2 ]
Zhang, Chen [3 ]
机构
[1] Purdue Univ, Krannert Grad Sch Management, W Lafayette, IN 47907 USA
[2] Hong Kong Univ Sci & Technol, Sch Business, Kowloon, Hong Kong, Peoples R China
[3] Univ Memphis, Fogelman Coll Business & Econ, Memphis, TN 38152 USA
关键词
open source software development (OSSD); team formation; developer social networks; collaborative ties;
D O I
10.1287/isre.1080.0192
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Recent years have witnessed a surge in self-organizing voluntary teams collaborating online to produce goods and services. Motivated by this phenomenon, this research investigates how these teams are formed and how individuals make decisions about which teams to join in the context of open source software development (OSSD). The focus of this paper is to explore how the collaborative network affects developers' choice of newly initiated OSS projects to participate in. More specifically, by analyzing software project data from real-world OSSD projects, we empirically test the impact of past collaborative ties with and perceived status of project members in the network on the self-assembly of OSSD teams. Overall, we find that a developer is more likely to join a project when he has strong collaborative ties with its initiator. We also find that perceived status of the noninitiator members of a project influences its probability of attracting developers. We discuss the implications of our results with respect to self-organizing teams and OSSD.
引用
收藏
页码:369 / 391
页数:23
相关论文
共 50 条
  • [41] The impact of leadership styles and motivations: lessons from Open Source Software projects for educational organizations
    Jose Racero, F.
    Bueno, Salvador
    Dolores Gallego, M.
    [J]. TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT, 2022, 34 (12) : 1449 - 1463
  • [42] Analyzing static structure of large software systems - Based on data from Open-Source Mozilla Project
    Fawcett, JW
    Gungor, MK
    Iyer, AV
    [J]. SERP '05: Proceedings of the 2005 International Conference on Software Engineering Research and Practice, Vols 1 and 2, 2005, : 491 - 496
  • [43] Networks, Social Influence, and the Choice Among Competing Innovations: Insights from Open Source Software Licenses
    Singh, Param Vir
    Phelps, Corey
    [J]. INFORMATION SYSTEMS RESEARCH, 2013, 24 (03) : 539 - 560
  • [44] Retrieving Similar Software from Large-scale Open-source Repository by Constructing Representation of Project Description
    Li, Chuanyi
    Ge, Jidong
    Chang, Victor
    Luo, Bin
    [J]. PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, BIG DATA AND SECURITY (IOTBDS), 2020, : 296 - 303
  • [45] New variants of lift-and-project cut generation from the LP tableau: Open source implementation and testing
    Balas, Egon
    Bonami, Pierre
    [J]. INTEGER PROGRAMMING AND COMBINATORIAL OPTIMIZATION, PROCEEDINGS, 2007, 4513 : 89 - +
  • [46] Generating lift-and-project cuts from the LP simplex tableau: Open source implementation and testing of new variants
    Balas E.
    Bonami P.
    [J]. Mathematical Programming Computation, 2009, 1 (2-3) : 165 - 199
  • [47] Qiber3D-an open-source software package for the quantitative analysis of networks from 3D image stacks
    Jaeschke, Anna
    Eckert, Hagen
    Bray, Laura J.
    [J]. GIGASCIENCE, 2022, 11
  • [48] Qiber3D-an open-source software package for the quantitative analysis of networks from 3D image stacks
    Jaeschke, Anna
    Eckert, Hagen
    Bray, Laura J.
    [J]. GIGASCIENCE, 2022, 11
  • [49] Qiber3D-an open-source software package for the quantitative analysis of networks from 3D image stacks
    Jaeschke, Anna
    Eckert, Hagen
    Bray, Laura J.
    [J]. GIGASCIENCE, 2022, 11
  • [50] Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks
    Sarkar A.
    Vandenhirtz J.
    Nagy J.
    Bacsa D.
    Riley M.
    [J]. SN Computer Science, 2021, 2 (3)