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Modeling Relational Events: A Case study on an Open Source Software Project

Modeling Relational Events: A Case study on an Open Source Software Project

Quintane, Eric, Conaldi, Guido ORCID: 0000-0003-3552-7307, Tonellato, Marco and Lomi, Alessandro (2014) Modeling Relational Events: A Case study on an Open Source Software Project. Organizational Research Methods, 17 (1). pp. 23-50. ISSN 1094-4281 (Print), 1552-7425 (Online) (doi:

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Sequences of relational events underlie much empirical research on organizational relations. Yet relational event data are typically aggregated and dichotomized to derive networks that can be analyzed with specialized statistical methods. Transforming sequences of relational events into binary network ties entails two main limitations: the loss of information about the order and number of events that compose each tie and the inability to account for compositional changes in the set of actors and/or recipients. In this article, we introduce a newly developed class of statistical models that enables researchers to exploit the full information contained in sequences of relational events. We propose an extension of the models to cater for sequences of relational events linking different sets of actors. We illustrate the empirical application of relational event models in the context of a free/open source software project with the aim to explain the level of effort produced by contributors to the project. We offer guidance in the interpretation of model parameters by characterizing the social processes underlying organizational problem solving. We discuss the applicability of relational events models in organizational research.

Item Type: Article
Uncontrolled Keywords: Relational event models, Temporal dependence, Two-mode networks, Free/open source software
Faculty / Department / Research Group: Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified: 04 Mar 2019 16:48
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: None
Selected for REF2021: None

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