Assessing the Structural Fluidity of Virtual Organizations and Its Effects

Abstract

AbstractA major advantage of Virtual Organizations (VOs) is flexible membership and participation. VO members are able to join and leave VOs at will, and can change whom they collaborate with at any point in time. Such flexibility may make VOs more efficient in the completion of collaborative work than traditional organizations. However, efficiency is only one of several measures of organizational performance; and flexibility in a virtual organization includes both how VO structures may be more fluid and adaptive, and how VO leadership emerges and evolves throughout the VO lifecycle. The aim of this chapter is to: (1) define and quantitatively assess the actual flexibility of participation in VOs, through a social network index that we call structural fluidity; and (2) measure the relationship between fluidity and performance in the work carried out within the VO. These are essential insights for the development of theories to guide the formation, development and dissolution of VOs, and teams that emerge around VO work. To accomplish these aims, we will apply a methodological approach and ontology for the study of VOs that we have used in over a dozen published studies, and refer to as Group Informatics. Our approach enables a comprehensive, interdisciplinary inquiry into the relationship between structural fluidity and performance in diverse VOs. Specifically, we will examine VOs in software engineering, disaster relief, online learning and public discourse communities that emerge through social media. We will apply Group Informatics to the design, development and testing of empirically and theoretically grounded algorithms for measuring VO fluidity and performance in each context, which will result in new theoretical advances that enable sophisticated analysis of the resulting data.KeywordsControl ChartBetweenness CentralityDisaster ReliefVirtual OrganizationTrace DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.