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AbstractCrisis response involves extensive planning and coordination within and across a multitude of agencies and organisations. This study explores how on‐the‐ground crisis response efforts align with crisis response guidelines. These guidelines are key to the effectiveness of crisis response. To this end, we construct, analyse and compare emergency response networks by using network analysis and natural language processing methods. Differences between plans and practice, that is, false positives (actions delivered but not prescribed) and false negatives (actions prescribed but not delivered), can impact response evaluation and policy revisions. We investigate collaboration networks at the federal, state and local level extracted from official documents (prescribed networks) and empirical data (observed networks) in the form of situational reports (n = 109) and tweets (n = 28,050) from responses to major hurricanes that made landfall in the United States. Our analyses reveal meaningful differences between prescribed and observed collaboration networks (mean node overlap ~9.94%, edge overlap ~3.94%). The observed networks most closely resemble federal‐level networks in terms of node and edge overlap, highlighting the prioritisation of federal response guidelines. We also observed a high ratio of false positives, that is, nongovernmental, nonprofit and volunteer organizations, that play a critical role in crisis response and are not mentioned in response plans. These findings enable us to evaluate the current best practices for response and inform emergency response policy planning.

Structural balance theory assumes triads in networks to gravitate toward stable configurations. The theory has been verified for undirected graphs. Since real-world social networks are often directed, we introduce a novel method for considering both transitivity and sign consistency for calculating balance in signed digraphs. We test our approach on graphs that we constructed by using different methods for identifying edge signs: natural language processing to infer signs from underlying text data, and self-reported survey data. Our results show that for various social contexts and edge sign detection methods, balance is moderately high, ranging from 61% to 96%. This paper makes three contributions: First, we extend the theory of structural balance to include signed digraphs where both transitivity and sign consistency are required and considered for calculating balance in triads with signed and directed edges. This improves the modeling of communication networks and other organizational networks where ties might be directed. Second, we show how to construct and analyze email networks from unstructured text data, using natural language processing methods to infer two different types of edge signs from emails authored by nodes. Third, we empirically assess balance in two different and contemporary contexts, namely remote communication in two business organizations, and team-based interactions in a virtual environment. We find empirical evidence in support of structural balance theory across these contexts.

Research shows that teams comprised of individuals with differing knowledge are increasingly important to enabling innovation in organizations. Beyond diverse connections, research also shows individuals must be familiar with their collaborators' areas of expertise to effectively integrate knowledge. Despite growing recognition of the importance of familiarity for interdisciplinary collaboration, we argue that there is reason to suspect this form of relationship is likely to be particularly rare in organizations. We present an egocentric analysis of collaboration networks in a scientific organization, exploring factors associated with the copresence of interdisciplinary ties alongside familiarity with a collaborator's area of expertise. Our results demonstrate pressures toward similarity of expertise that minimized connections to differing alters. Furthermore, those respondents who had diverse connections tended to be unfamiliar with their distant collaborators' domains. Interaction counteracted this effect but participants reported pressures inhibiting interaction across knowledge boundaries. The findings demonstrate how network forces compound to inhibit what we call "different yet familiar" ties and, by doing so, offer conceptual and practical implications for contemporary organizations.