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

Abstract

Previous research on interaction behavior among organizations (resource exchange, collaboration, communication) has typically aggregated those behaviors over time as a network of organizational relationships. We instead study structural-temporal patterns in organizational exchange, focusing on the dynamics of reciprocation. Applying this lens to a community of Italian hospitals during the period 2003-2007, we observe two mechanisms of interorganizational reciprocation: organizational embedding and resource dependence. We show how these two mechanisms operate on distinct time frames: Dependence applies to contemporaneous exchange structures, whereas embedding develops through longer-term historical patterns. We also show how these processes operate differently in competitive and noncompetitive contexts, operationalized in terms of market differentiation and geographic space. In noncompetitive contexts, we 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, we find no reciprocation and instead observe the microfoundations of status hierarchies. INTRODUCTION

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

“…We believe that this is a good time for proposing a new method to enrich the toolbox of social scientists for the analysis of network data, given the constantly growing interest in analyzing relational events over the past decade. Recent examples include the study of communicational dynamics of animals (Tranmer et al 2015) and emergency responders (Butts 2008), brokerage and receiver choice in communication networks (Quintane and Carnabuci 2016;Stadtfeld, Geyer-Schulz, and Allmendinger 2011), e-mail communication in organizations (Perry and Wolfe 2013), interaction within teams (Leenders, Contractor, and DeChurch 2016), exchange of patients between hospitals (Kitts et al 2017;Vu et al 2017), and collaboration on online platforms (Vu et al 2011). Further, international conflict relations were analyzed in a relational event framework (Lerner et al 2013).…”

Computational Conflict Research

“…The stratified Cox model estimates which factors affect event occurrence, i.e., cause an event to occur during one particular strata at time t, and assumes that the baseline hazard of each event is constant within a stratum but varies between strata (Cox and Oakes, 1984;Allison, 1982;Box-Steffensmeier and Jones, 2004;Allison, 2014). The stratified Cox model with constant event times can be estimated with a conditional logistic regression (Gail et al, 1980;Allison, 1982) and has become the most widely used model for REMs (Kitts et al, 2016;Quintane et al, 2014;Vu et al, 2015). In the conditional logistic regression each stratum (or risk set) compares true events, set to 1, to null events, set to 0.…”

“…REMs are inferential models that make use of temporally fine-grained records of social interactions to model complex interaction patterns and endogenous processes. REMs can be used to detect social influencing (Malang et al, 2018), understand social exchanges (Butts, 2008;Zenk and Stadtfeld, 2010;Quintane et al, 2014;Kitts et al, 2016;Stadtfeld and Geyer-Schulz, 2011), and determine causes for group or conflict formation processes (Lerner et al, 2013a;Leifeld and Brandenberger, 2019;De Nooy and Kleinnijenhuis, 2013). Building on event history analysis, REMs try to explain the occurrence of relational events.…”

Trading favors—Examining the temporal dynamics of reciprocity in congressional collaborations using relational event models

“…Reciprocity is often referred to as one of the guiding norms for social interactions (Gouldner, 1960;Emerson, 1976). While many di↵erent theories of social exchanges and interactions state that reciprocity fosters under repeated actions (Gouldner, 1960;Melamed and Simpson, 2016) and builds trust and cohesion over time (Friedkin, 2004;Molm, Schaefer and Collett, 2007), few studies examine reciprocity at the micro-level as a dynamic mechanism in relational event sequences (e.g., Quintane et al, 2013;Kitts et al, 2016). How do nodes in a network react to network changes initiated by other nodes surrounding them that can be classified as reciprocated favors?…”

“…First introduced by , relational event models (REMs) can be used to model sequences of network events (or event streams). More and more studies are using event sequences to understand the evolution of network structures (Vu et al, 2011;Zenk and Stadtfeld, 2010; De Nooy and Kleinnijenhuis, 2013;Lerner, Bussmann, Snijders and Brandes, 2013; DuBois, Butts and Smyth, 2013;Quintane et al, 2013;Liang, 2014;Patison et al, 2015;Tranmer et al, 2015;Welbers and de Nooy, 2014;Kitts et al, 2016;Leenders, Contractor and DeChurch, 2016;Pilny et al, 2016;Xia, Mankad and Michailidis, 2016;Quintane and Carnabuci, 2016;Pilny et al, 2017). However, few studies examine two-mode network event sequences (De Nooy, 2011;Stadtfeld and Geyer-Schulz, 2011;Quintane et al, 2014;Malang, Brandenberger and Leifeld, 2017).…”