Michael D. Siciliano

It’s the Quality Not the Quantity of Ties That Matters: Social Networks and Self-Efficacy Beliefs

This study explores the role of knowledge access and peer influence as mechanisms by which networks may shape teacher self-efficacy. The basic premise is twofold: (a) that peer interaction provides opportunities to access teaching relevant knowledge and thus may reduce uncertainty and (b) that self-efficacy beliefs may be shaped by the efficacy beliefs of the peers one is directly connected to in the advice network. The results suggest that both mechanisms may shape teacher self-efficacy and that the quality of ties, rather than the quantity, may have a stronger influence on self-efficacy. These findings offer new insight into the potential ways in which social networks and collegial interaction influence teacher beliefs.

Estimating network structure via random sampling: Cognitive social structures and the adaptive threshold method

This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals’ perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings.

Professional Networks and Street-Level Performance How Public School Teachers’ Advice Networks Influence Student Performance

Given the complexity of their work, street-level bureaucrats rely on their professional networks to access implementation resources and information. Despite the acknowledged importance of these networks, little research exists on how network structure and composition influence frontline performance. This study analyzes a unique data set that includes the professional networks of more than 420 teachers in 21 public schools along with 3 years of administrative data on student test scores and student demographics. Using value-added models derived from the student test data, objective measures of teacher performance were calculated. The results suggest that street-level performance is influenced by both network structure and composition. Thus, the actions of street-level workers are not independent responses to individual dilemmas, but rather are developed and shaped by specific features of the social structure in which the individual bureaucrat is embedded.

Network Features and Processes as Determinants of Organizational Interaction during Extreme Events

Despite the widely acknowledged importance of collaboration among participants in governance networks, a limited number of studies have attempted to statistically model the processes by which those networks form. In this article, we explore a range of network features and processes and measure their influence on network formation. We examine the case of Hurricane Katrina and employ exponential random graph models to identify the drivers of network formation in extreme events. We find that both the attributes of individual organizations and endogenous network processes affect organizational collaboration. Understanding these factors is important because the structure of the response network influences information flow, resource exchange, and performance.

If You Are Committed, Then so Am I The Role of Social Networks and Social Influence on Organizational Commitment

Research on organizational commitment has generally ignored the role that social networks may play in shaping work-related attitudes. In this article, we explore two network-based mechanisms: (a) structural position effects, based on centrality, and (b) social influence, based on direct social contact with peers. Relying on network, survey, and administrative data for more than 400 employees in 21 different organizations, our findings suggest that both structural position and social influence are associated with organizational commitment. These findings have important implications for how public organizations structure work teams, hire and promote employees, and engage in reform efforts.

The Formation of Transnational Knowledge Networks on Social Media

ABSTRACT Transnational knowledge networks provide organizations with information useful in addressing shared problems. Social media may enable the formation of those networks, yet their role in the process has received little attention. This article examines the structure and antecedents of two networks facilitated by the microblogging platform Twitter operating in the policy domain of emergency management. One network includes national-level government agencies responsible for disaster response and recovery operations; the other includes nongovernmental organizations in the form of Red Cross and Red Crescent national societies. We use a logistic regression quadratic assignment procedure to test hypotheses derived from related literature. While findings indicate that shared language and geographic proximity shaped network formation, both networks exhibit boundary spanning behavior in which organizations sought out information from high-profile, resource-rich organizations. Those organizations helped to connect otherwise regionally bound clusters and demonstrate the nascent potential of social media to create global transnational knowledge networks.

Network Formation During Disasters: Exploring Micro-Level Interorganizational Processes and the Role of National Capacity

ABSTRACT Despite common assumptions about the processes associated with interorganizational network formation, the resulting structures and relevant factors often vary. This variation suggests that there are likely contextual or meta-network variables that moderate the influence of well-established micro-level mechanisms. Because much of the existing research on disaster response networks relies on single case studies, the role of meta-network variables in shaping network formation remains unexplored. We look to fill this gap by comparing network formation patterns in multiple countries that vary in their disaster management capacity. This article uses social network analysis to analyze the formation of response networks after earthquakes in Indonesia, Haiti, and Japan. This study contributes to existing literature by examining how transitivity, homophily, and brokerage vary in their salience under different macro-level constraints. The results suggest that national response capacity may influence the jurisdictional level at which bridging and bonding strategies take place.

Network exploration and exploitation: Professional network churn and scientific production

Abstract The production of scientific knowledge is an inherently social process making professional networks important for producing science outcomes. Although prior work has demonstrated the connection between collaboration and productivity, most research that examines scientist networks begins from the perspective that structure predicts productivity. Institutional approaches to explaining productivity are useful, but generally ignore the role of individual agency or strategic network behavior. Our study utilizes the dynamic perspective of network churn to assess how professional network composition and structure change overtime via processes of network exploration and exploitation. Using two waves of survey data from a national sample of academic scientists and engineers across six disciplines in the United States, we investigate how network churn affects the quantity and quality of scientific production. Our results suggest that while network exploration generally improves production quality, it can hurt quantity. Network exploitation tends to have the opposite effect, resulting in short term gains but potentially limiting the innovativeness of future research. By recognizing the tradeoffs associated with alternative networking strategies, policy makers in universities and other research organizations can begin focusing on interventions that more effectively target scientists’ strategic network behavior.

GUIDES: Geospatial Urban Infrastructure Data Engineering Solutions

As the underground infrastructure systems of cities age, maintenance and repair become an increasing concern. Cities face difficulties in planning maintenance, predicting and responding to infrastructure related issues, and in realizing their vision to be a smart city due to their incomplete understanding of the existing state of the infrastructure. Only few cities have accurate and complete digital information on their underground infrastructure (e.g., electricity, water, natural gas) systems, which poses problems to those planning and performing construction projects. To address these issues, we introduce GUIDES as a new data conversion and management framework for urban underground infrastructure systems that enable city administrators, workers, and contractors along with the general public and other users to query digitized and integrated data to make smarter decisions. This demo paper presents the GUIDES architecture and describes two of its central components: (i) mapping of underground infrastructure systems, and (ii) integration of heterogeneous geospatial data.