Abstract not found

Research utilizing the perspective of social networks can shed important light on political processes, as is illustrated by the other articles in this issue and also the special issue Social Networks and American Politics of American Politics Research (Heaney and McClurg, 2009). This perspective offers some complications for statistical analysis, however. A network approach is so useful because it can represent the interdependence between political actors (see Huckfeldt, 2009) – but statistical modeling is commonly based on independence assumptions. The challenge in statistical modeling of social network data is to represent the dependencies between network ties so that valid inferences can be obtained and misspecification avoided; and, by doing so, to provide methods that allow researchers to test hypotheses about these interdependencies. This article treats statistical methods for network panel data. It is assumed that the reader has a basic knowledge of networks and the associated terminology; see, e.g., Wasserman and Faust (1994) or Knoke and Yang (2008). For the data structure it is assumed that a fixed set of nodes is being considered – where, however, exceptions are allowed in the sense that some nodes may enter or leave the network – while the change

In a previous MCQ article, Monge et al. overviewed the fundamental concepts and processes of evolutionary theory and their applications to key issues in organizational communication. This article extends that work by providing an overview of research tools for studying organizational ecology and evolution, including (a) the variation-selection-retention sequence, (b) the likelihood of events occurring over a period of time (event history analysis), (c) transition sequence of populations from one state to another (sequence analysis), (d) relationships among nodes in networks over time (network analysis), (e) simulation of complex relationships and interactions

The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007

The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007

In this paper, we study the formation of network ties between Örms along the life cycle of a creative industry. We focus on three drivers of network formation: i) network endogeneity which stresses a path-dependent change originating from previous network structures, ii) Öve forms of proximity (e.g. geographical proximity) which ascribe tie formation to the similarity of actorsí attributes; and (iii) individual characteristics which refer to the heterogeneity in actors capabilities to exploit external knowledge. The paper employs a stochastic actor-oriented model to estimate the ñ changing ñ e§ects of these drivers on inter-Örm network formation in the global video game industry from 1987 to 2007. Our Öndings indicate that the e§ects of the drivers of network formation change with the degree of maturity of the industry. To an increasing extent, video game Örms tend to partner over shorter distances and with more cognitively similar Örms as the industry evolves.

The dynamics of interfirm networks along the industry life cycle: The case of the global video game industry, 1987–2007

Abstract not found

We propose a sensor-based organizational design and engineering approach that combines behavioral sensor data with other sources of information such as e-mail, surveys, and performance data in order to design interventions aimed at improving organizational outcomes. The proposed system combines sensor measurements, pattern recognition algorithms, simulation and optimization techniques, social network analysis, and feedback mechanisms that aim at continuously monitoring and improving individual and group performance. We describe the system’s general specifications and discuss several studies that we conducted in different organizations using the sociometric badge experimental sensing platform. We have deployed such system under naturalistic settings in more than ten organizations up to this date. We show that it is possible to automatically capture group dynamics, and analyze the relationship between organizational behaviors and both subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured by sensors, in order to optimize group configurations that maximize individual

Collaboration networks, proximity, dynamic network models, R&D