Beyond Networks in Structural Theories of Exchange: Promises from Computational Social Science

Abstract

Purpose À The research community currently employs four very different versions of the social network concept: A social network is seen as a set of socially constructed role relations (e.g., friends, business partners), a set of interpersonal sentiments (e.g., liking, trust), a pattern of behavioral social interaction (e.g., conversations, citations), or an opportunity structure for exchange. Researchers conventionally assume these conceptualizations are interchangeable as social ties, and some employ composite measures that aim to capture more than one dimension. Even so, important discrepancies often appear for non-ties (as dyads where a specific role relation or sentiment is not reported, a specific form of interaction is not observed, or exchange is not possible).

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

“…The advent of electronic communication, social media, and human sensor technologies have brought about the possibility to collect a wealth of fine-grained social interaction data. Interdisciplinary researchers have called for using such data to enrich the empirical toolbox of social scientists (Lazer et al 2009;Kitts 2014). Besides these newly emerging, technology-driven advances, for many years social networks researchers have been collecting archival network data about, for example, political and financial relations that often contain detailed information about the timing of relational interaction.…”

Step by step: Capturing the dynamics of work team process through relational event sequences

“…The results of the research suggest the promise of a more processual, sequential, and microdynamic perspective regarding networks and organizational behavior (e.g., Ahuja, Soda, & Zaheer, ). Indeed, an event‐based framework has also been suggested in the communication (Hewes & Poole, ), group (Kitts, ), and management (Van de Ven & Poole, , ) literature, with interactions framed as an unfolding series of events. After all, communication is inherently a process defined by a complex series of events (Poole, ).…”

“…However, this theme is only really implied in the above‐mentioned studies. Building on the growing literature connecting events and processes, we propose a process‐oriented relational event‐based framework for studying emergence and argue that analyzing interaction event patterns over time has both theoretical and computational advantages over prior methods (Kitts, ; Kozlowski, Chao, Grand, Braun, & Kuljanin, ). More specifically, the current research builds on past like‐minded approaches to emergence by including a higher order outcome term in the model itself.…”

Modeling Network Dynamics

“…With the widespread accumulation of electronic trails such data is within reach. (Kitts 2014;Bahulkar et al 2017). Moreover, recent advances in modeling hierarchical network data may be generalizable to incorporate such temporal nesting data structures (Lazega & Snijders 2016).…”