Francesca Pallotti

Relational collaboration among spatial multipoint competitors

The presence of network ties between multipoint competitors is frequently assumed but rarely examined directly. The outcomes of multipoint competition, therefore, are better understood than their underlying relational mechanisms. Using original fieldwork and data that we have collected on an interorganizational network of patient transfer relations within a regional community of hospitals, we report and interpret estimates of Exponential Random Graph Models (ERGM) that specify the probability of observing network ties between organizations as a function of the degree of their spatial multipoint contact. We find that hospitals competing more intensely for patients across multiple geographical segments of their market (spatial multipoint competitors) are significantly more likely to collaborate. This conclusion is robust to alternative explanations for the formation of network ties based on organizational size differences, resource complementarities, performance differentials, and capacity constraints. We show that interorganizational networks between spatial multipoint competitors are characterized by clear tendencies toward clustering and a global core-periphery structure arising as consequences of multiple mechanisms of triadic closure operating simultaneously. We conclude that the effects of competition on the structure of interorganizational fields depends on how markets as physical and social settings are connected by cross-cutting network ties between competitors.

Quality of Care and Interhospital Collaboration: A study of Patient Transfers in Italy

Objectives:We examine the dynamics of patient-sharing relations within an Italian regional community of 35 hospitals serving approximately 1,300,000 people. We test whether interorganizational relations provide individual patients access to higher quality providers of care. Research Design and Methods:We reconstruct the complete temporal sequence of the 3461 consecutive interhospital patient-sharing events observed between each pair of hospitals in the community during 2005–2008. We distinguish between transfers occurring between and within different medical specialties. We estimate newly derived models for relational event sequences that allow us to control for the most common forms of network-like dependencies that are known to characterize collaborative relations between hospitals. We use 45-day risk-adjusted readmission rate as a proxy for hospital quality. Results:After controls (eg, geographical distance, size, and the existence of prior collaborative relations), we find that patients flow from less to more capable hospitals. We show that this result holds for patient being shared both between as well as within medical specialties. Nonetheless there are strong and persistent other organizational and relational effects driving transfers. Conclusions:Decentralized patient-sharing decisions taken by the 35 hospitals give rise to a system of collaborative interorganizational arrangements that allow the patient to access hospitals delivering a higher quality of care. This result is relevant for health care policy because it suggests that collaborative relations between hospitals may produce desirable outcomes both for individual patients, and for regional health care systems.

The embeddedness of organizational performance: Multiple membership multiple classification models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical merits of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and partially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteristics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covariates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures.

Assimilation and differentiation: A multilevel perspective on organizational and network change

This 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.

How Far Do Network Effects Spill Over? Evidence from an Empirical Study of Performance Differentials in Interorganizational Networks

Organizations join interorganizational networks in the hope of gaining exposure to learning opportunities, and accessing valuable extramural resources and knowledge. In this paper we argue that participation in interorganizational networks also reduces performance differentials among organizational nodes. We examine three alternative mechanisms capable of sustaining this prediction. The first (strength of ties) operates at a strictly local level defined in terms of dyadic relations linking organizations. The second mechanism (social proximity) operates at an intermediate – or meso level of interdependence defined in terms of membership in overlapping cliques into which interorganizational networks are typically organized. The third mechanism (structural equivalence) is global and pertains to jointly occupied network positions. The objective of this paper is to examine at which of these levels network effects operate to reduce performance differentials among members of interorganizational networks. Our empirical analysis of performance differentials between hospitals in a regional community supports the following conclusions: (i) performance spillover effects are highly differentiated and vary significantly across network levels; (ii) organizations occupying similar positions within the network are more similar in terms of performance; (iii) joint membership in multiple sub-groups (or cliques) reduces performance differentials up to a limit; after this limit is reached, the performance of organizational partners begins to diverge; (iv) the strength of direct collaboration between organizational partners does not necessarily reduce interorganizational performance differentials. The results of the study are new because available research on interorganizational networks says little about the range of network effects, i.e., about how far the performance spillover effects that operate through networks propagate throughout organizational fields and communities. These results are also consequential because they suggest that network effects on performance differentials are sensitive to the specification of network boundaries.

Relational event models for longitudinal network data with an application to interhospital patient transfers

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfers within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available. Copyright

Exponential Random Graph Models for Social Networks: How To Close a Hole: Exploring Alternative Closure Mechanisms in Interorganizational Networks

One way to think about social networks is as social structures built from the bottom up through combinations of simpler components defined in terms of local configurations of ties, or “motifs” (Milo et al., 2002; Pattison & Robins, 2002). Local configurations of network ties may be interpreted as observable outcomes of specific social mechanisms such as reciprocity. Because organizations display a strong tendency toward forming ties with their partner’s partners, processes of tie maintenance and formation based on closure mechanisms have been of particular interest to scholars of interorganizational networks (Gulati & Gargiulo, 1999; Hallen, 2008; Laumann & Marsden, 1982; Lomi & Pattison, 2006). Closure has been found to shape the formation and maintenance of network ties between organizations operating in a variety of empirical settings ranging from manufacturing relations in the automotive industry (Lomi & Pattison, 2006), to strategic alliances in various industrial sectors (Gulati & Gargiulo, 1999), to equity relations between organizations belonging to the same “keiretsu” (Lincoln, Gerlach, & Ahmadjian, 1996). The accumulation of empirical experiences in the study of interorganizational relations has given shape to the general expectation that partners of partners are (more likely to be) partners. What social mechanisms may be underlying such expectations? In theoretical terms, the tendency toward transitive closure in interorganizational networks has been framed and interpreted as the direct

Investigating the Temporal Dynamics of Interorganizational Exchange: Patient Transfers among Italian Hospitals1

Previous research on interaction behavior among organizations (resource exchange, collaboration, communication) has typically aggregated those behaviors over time as a network of organizational relationships. The authors instead study structural-temporal patterns in organizational exchange, focusing on the dynamics of reciprocation. Applying this lens to a community of Italian hospitals during 2003–7, the authors observe two mechanisms of interorganizational reciprocation: organizational embedding and resource dependence. The authors show how these two mechanisms operate on distinct time horizons: dependence applies to contemporaneous exchange structures, whereas embedding develops through longer-term historical patterns. They also show how these processes operate differently in competitive and noncompetitive contexts, operationalized in terms of market differentiation and geographic space. In noncompetitive contexts, the authors observe both logics of reciprocation, dependence in the short term and embedding over the long term, developing into population-level generalized exchange. In competitive contexts, they find no reciprocation and instead observe the microfoundations of status hierarchies.

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ABSTRACT Body image issues associated with eating disorders involve attitudinal and perceptual components: individuals’ dissatisfaction with body shape or weight, and inability to assess body size correctly. While prior research has mainly explored social pressures produced by the media, fashion, and advertising industries, this paper focuses on the effects of personal networks on body image, particularly in the context of internet communities. We use data collected on a sample of participants to websites on eating disorders, and map their personal networks. We specify and estimate a model for the joint distribution of attitudinal and perceptual components of body image as a function of network-related characteristics and attributional factors. Supported by information gathered through in-depth interviews, the empirical estimates provide evidence that personal networks can be conducive to positive body image development, and that the influence of personal networks varies significantly by body size. We situate our discussion in current debates about the effects of computer-mediated and face-to-face communication networks on eating disorders and related behaviors.

Multilevel Structure of Interorganizational Networks: Theoretical Mechanisms and Empirical Tests

A distinctive feature of formal organizations as social actors is their changeable internal structure. Yet, studies of interorganizational networks typically take internal organizational structure ...