From Ties to Events in the Analysis of Interorganizational Exchange Relations

Abstract

Relational event models expand the analytical possibilities of existing statistical models for interorganizational networks by: (i) making efficient use of information contained in the sequential ordering of observed events connecting sending and receiving units; (ii) accounting for the intensity of the relation between exchange partners, and (iii) distinguishing between short- and long-term network effects. We introduce a recently developed relational event model (REM) for the analysis of continuously observed interorganizational exchange relations. The combination of efficient sampling algorithms and sender-based stratification makes the models that we present particularly useful for the analysis of very large samples of relational event data generated by interaction among heterogeneous actors. We demonstrate the empirical value of event-oriented network models in two different settings for interorganizational exchange relations—that is, high-frequency overnight transactions among European banks and patient-sharing relations within a community of Italian hospitals. We focus on patterns of direct and generalized reciprocity while accounting for more complex forms of dependence present in the data. Empirical results suggest that distinguishing between degree- and intensity-based network effects, and between short- and long-term effects is crucial to our understanding of the dynamics of interorganizational dependence and exchange relations. We discuss the general implications of these results for the analysis of social interaction data routinely collected in organizational research to examine the evolutionary dynamics of social networks within and between organizations.

Multiple clocks in network evolution

“…To the best of our knowledge, this is also one of the few studies currently available offering a systemic empirical demonstration of the contextual viability of network mechanisms due to variations in their internal time structure. Extant research has recognized the variability of network effects (Amati et al, 2019; Bianchi and Lomi, 2022; Bianchi et al, 2020; Lomi and Bianchi, 2022; Zappa and Vu, 2021), but has not connected it to differences in the speed at which the different dependence mechanisms operate.…”

“…Bianchi et al (2020) have proposed a longitudinal network model based on latent trajectories to study the evolution of reciprocal giving behaviors in consequence of the exogenous shocks that have severely affected the European interbank market since 2008. Applications of relational event models to the analysis of networks of financial transaction have emerged more recently (Bianchi and Lomi, 2022; Lomi and Bianchi, 2022; Zappa and Vu, 2021).…”

“…We pursue these interdependent objectives in the context of data that we have collected on interorganizational exchange and dependence relations coordinated through a market interface designed to connect sellers and buyers of liquidity (Lomi and Bianchi, 2022; Bianchi and Lomi, 2022; Bianchi et al, 2020). More specifically, we examine how repetition, reciprocation, generalized exchange, and transitivity mechanisms shape—and emerge from time-ordered sequences of high-frequency financial transactions.…”

How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models

“…The effect of time recency of past interactions was discussed by Tranmer et al (2015), and a weekend effect was investigated by Amati et al (2019) in a network of health care organizations, in which authors show the different network mechanisms that can be observed between week days and weekends. In another work, Bianchi & Lomi (2022) study short-term and long-term effects in network dynamics and provide examples on a highfrequency network (financial markets) as well as on a low-frequency network (patient-sharing relations among health care organizations). Furthermore, methods for estimating time-varying networks effects were proposed by Mulder & Leenders (2019), using moving window approaches, and by Fritz et al (2021) using B-splines.…”

Managing interorganizational renqing practices in high-performance organizations in Taiwan: the when, where and how of renqing giving

“…, 2022) and execute effective business strategies (Zolkiewski and Feng, 2012). It also calls for a theoretically nuanced understanding of interorganizational reciprocity that has been lacking within management theory (Bianchi and Lomi, in press). It entails building a multi-level theory, just like in the case of interorganizational guanxi (Kuo et al.…”