1,721,055 research outputs found
Assimilation and differentiation: A multilevel perspective on organizational and network change
Full text linkThis paper builds on recently derived stochastic actor-oriented models (SAOMs) for the coevolution of one-mode and two-mode networks, and extends them to the analysis of how concurrent multilevel processes of (internal) organizational and (external) network change affect one another over time. New effects are presented that afford specification and identification of two apparently conflicting micro-relational mechanisms that jointly affect decisions to modify the portfolio of internal organizational activities. The first mechanism, assimilation, makes network partners more similar by facilitating the replication and diffusion of experience. The second mechanism, functional differentiation, operates to maintain and amplify differences between network partners by preventing or limiting internal organizational change. We illustrate the empirical value of the model in the context of data that we have collected on a regional community of hospital organizations connected by collaborative patient transfer relations observed over a period of seven years. We find that processes of social influence conveyed by network ties may lead both to similarity and differences among connected organizations. We discuss the implications of the results in the context of current research on interorganizational networks
The Partners of My Partners: Shared Collaborative Experience and Team Performance in Surgical Teams
Full text linkWhen teams in organizations are assembled to perform contingent tasks, team members carry with them experiences of prior interaction with partners in different teams. Focal team members share collaborative experiences to the extent that they worked with common external prior partners. Extending current research on team effectiveness, we investigate how shared collaborative experience (SCE) affects team performance. Consistent with the established understanding of team processes as carrying both a teamwork and a taskwork component, we conceptualize SCE as having two distinct dimensions that we call SCE extent and SCE diversity. We posit that high SCE extent increases the ability of teams to refine their teamwork processes, increasing their performance through enhanced coordination and reflexivity. We argue that high SCE diversity hinders the ability of teams to form a shared understanding of task demands, thus undermining team performance. Furthermore, we investigate the contingent effect of task complexity on the relationship between SCE and performance. We argue that the benefits of implicit coordination and the drawbacks of experience diversity decrease as tasks become more complex and require more explicit coordination and wider repertoires of responses. These predictions find support in an analysis of 1343 robot-assisted surgery operations performed by 114 surgeons during a four-year period in a private university hospital. By explicitly recognizing how team members benefit from the network of their shared prior partners, our study contributes to developing a new approach to study the effectiveness of temporary teams in organizations
Organized Anarchies and the Network Dynamics of Decision Opportunities in an Open Source Software Project
No full textWhen considered as organized solutions to problems of provision of public goods, Free/Open Source Software (F/OSS) productions share a number of their defining features with the organized anarchies described by Cohen, March and Olsen in their “Garbage Can Model” (GCM). The open and voluntary contribution of software developers creates constant fluctuations in levels of attention and an extremely fluid participation. The lack of predefined hierarchical access to organizational problems determines a fundamental uncertainty about how collective goals may be linked to individual activities, and in how responsibilities and tasks may be allocated efficiently within the project. Finally, the complexity involved in the collective production of tens of thousands of lines of computer code without explicit coordination creates a situation of technological ambiguity supported by a radically decentralized activity of organizational problem finding and problem solving. In this paper we take these broad similarities as point of departure to specify an empirical model that captures some of the garbage can properties of organizational problem-solving activities in the context of a specific F/OSS project followed throughout a complete release cycle. We examine the interconnected system of individual decisions emerging from problem-solving activities performed by the 135 contributors involved in the F/OSS project on the 719 software bugs reported during the period of observation. We treat the evolving two-mode network produced by encounters between carriers of organizational solutions (contributors) and organizational problems (software bugs) as a dynamic opportunity structure that constrains and enables organizational decision making. We document how stable local configurations linking problems and solutions are induced by – and at the same time sustain – decentralized problem-solving activities with meaningful self-organizing properties
Identity assimilation and social networks in organizations: an empirical study of social identities across multiple organizational targets
No full textIndividual identities emerge through membership in multiple categories and social groups. The process of identification underlying individual affiliation decisions is therefore central in our understanding of identity formation. In this paper we argue that network-based social processes affect identification of individuals with the organization. In this paper we test this argument and report clear evidence of identity assimilation: the identity of individuals within organizations tends to become similar to the identities of their network partners. Because organizations are hierarchical systems it is important to attend to the possibility that the effects of social networks are not uniform across hierarchical levels. In this work we show that the effects of social networks vary systematically across possible identification targets that organizational members confront
From network ties to network structures: exponential random graph models of interorganizational relations
No full textTheoretical accounts of network ties between organizations emphasize the interdependence of individual intentions, opportunities, and actions embedded in local configurations of network ties. These accounts are at odds with empirical models based on assumptions of independence between network ties. As a result, the relation between models for network ties and the observed network structure of interorganizational fields is problematic. Using original fieldwork and data that we have collected on collaborative network ties within a regional community of hospital organizations we estimate newly developed specifications of Exponential Random Graph Models (ERGM) that help to narrow the gap between theories and empirical models of interorganizational networks. After controlling for the main factors known to affect partner selection decisions, full models in which local dependencies between network ties are appropriately specified outperform restricted models in which such dependencies are left unspecified and only controlled for statistically. We use computational methods to show that networks based on empirical estimates produced by models accounting for local network dependencies reproduce with accuracy salient features of the global network structure that was actually observed. We show that models based on assumptions of independence between network ties do not. The results of the study suggest that mechanisms behind the formation of network ties between organizations are local, but their specification and identification depends on an accurate characterization of network structure. We discuss the implications of this view for current research on interorganizational networks, communities, and fields
Social Network Modeling
No full text The development of stochastic models for the analysis of social networks is an important growth area in contemporary statistics. The last few decades have witnessed the rapid development of a variety of statistical models capable of representing the global structure of an observed network in terms of underlying generating mechanisms. The distinctive feature of statistical models for social networks is their ability to represent directly the dependence relations that these mechanisms entail. In this review, we focus on models for single network observations, particularly on the family of exponential random graph models. After defining the models, we discuss issues of model specification, estimation and assessment. We then review model extensions for the analysis of other types of network data, provide an empirical example, and give a selective overview of empirical studies that have adopted the basic model and its many variants. We conclude with an outline of the current analytical challenges. </jats:p
Some days are better than others: Examining time-specific variation in the structuring of interorganizational relations
Full text linkThe generative mechanisms controlling change in interorganizational relations are typically assumed to be time-independent, i.e., operate homogeneously and synchronously over time. In this paper we consider some of the
implications of violating this assumption. We adopt and extend statistical models for relational events to reveal
time-specific variations in mechanisms underlying interorganizational relations observed within a small community
of health care organizations. We find that aggregate estimates of parameters associated with mechanisms
of theoretical interest mask fine-grained temporal variation in relational events sequences. We discuss the implications
of this result for studies of interorganizational relations – and social networks more generally
Fast Maximum Likelihood Estimation via Equilibrium Expectation for Large Network Data
Full text linkA major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes
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