Research on social capital has found that individuals who access resources through social relations gain competitive advantage and systems with high levels or desirable distributions of social capital are more effective. These effects depend on actors allocating resources to others in their social system at-large instead of to others with whom they share specific social relationships. It is hypothesized that actors who identify with others in a social system as a collective are more likely to allocate resources uniformly throughout the system. Thus, identification with the collective can serve as a quasi-tie, directing the allocation of resources, but not defined by specific social relations. Findings from a longitudinal, multilevel network study of teachers' use of computers support multiple theories of resource allocation and, in particular, confirm that teachers who identify with the collectives of their schools are less likely to favor close colleagues and close colleagues of close colleagues, in their provision of help.
AbstractThe validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors' preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.
The formatting of several of the equations appearing in this article made them difficult to interpret (Organization Science, Volume 10, Number 3, pp. 253–277 ). Following are reformatted versions of Equations (5), (6), and (8).
We define the complex system underlying organizational culture by incorporating the social-psychological principles of balance and information (B-I) into models of influence (changes in attitudes as a function of interaction) and selection (changes in interaction). We identify information based influence as a potential anchor for actors' sentiments so that they are not overwhelmed by normative influence. In the model of selection, we identify the pursuit of information as an important counterbalance to the effect of homophily (interacting with others like oneself). Using the tools of dynamic systems we show how our models generate the full range of equilibria of complex systems. Through simulations we also explore how our system responds to exogenous effects.
AbstractMuch of the impact of a policy depends on how it is implemented, especially as mediated by organizations such as schools or hospitals. Here, we focus on how implementation of evidence‐based practices in human service organizations (e.g., schools, hospitals) is affected by intraorganizational network dynamics. In particular, we hypothesize intraorganizational behavioral divergence and network polarization are likely to occur when actors strongly identify with their organizations. Using agent‐based models, we find that when organizational identification is high, external change agents who attempt to direct organizations by introducing policy aligned messages (e.g., professional development emphasizing specific teaching practices) may unintentionally contribute to divergence in practice and polarization in networks, inhibiting full implementation of the desired practices as well as reducing organizational capacity to absorb new practices. Thus, the external change agent should consider the interaction between the type of message and the intraorganizational network dynamics driven by organizational identification.
In diesem Kapitel präsentieren die Autoren die Soziale Netzwerkanalyse im Kontext aktueller Bildungsreformen, die sich auf Instruktionspraktiken von Lehrpersonen beziehen. Lehrpersonen spielen für die Implementation von Bildungsformen eine zentrale Rolle. Soziale Netzwerke von Lehrpersonen sind insofern von hoher Bedeutung, als Lehrpersonen im Zuge der Implikation neuer Praktiken auf lokales Wissen und lokale Normen zurückgreifen. Die Autoren beschreiben drei netzwerkanalytische Ansätze: Erstens präsentieren sie Netzwerkdaten graphisch, um die Struktur des Netzwerkes zu charakterisieren, durch die Information und Wissen über die Reform verbreitet werden. Zweitens verwenden sie soziale Einflussmodelle, um darzustellen, wie Überzeugungen und Verhalten von Lehrpersonen von denjenigen Lehrpersonen beeinflusst werden, mit denen sie interagieren. Drittens verwenden die Autoren soziale Selektionsmodelle, um darzustellen, wie Lehrpersonen die Personen auswählen, mit denen sie die Reform betreffend interagieren. Sie diskutieren Implikationen für den wissenschaftlichen Dialog, die Bedeutung für bildungspolitische Studien sowie die praktische Bedeutung für bildungspolitische Akteure und Schulangestellte. (DIPF/Orig.).;;;In this chapter the authors present social network analysis in the context of recent educational reforms concerning teachers' instructional practices. Teachers are critical to the implementation of educational reforms, and teacher networks are important because teachers draw on local knowledge and conform to local norms as they implement new practices. The authors describe three social network approaches. First, they graphically represent network data to characterize the network structure through which information and knowledge about reforms might diffuse. Second, they use social influence models to express how teachers' beliefs or behaviors are affected by others with whom they interact. Third, the authors use social selection models to express how teachers might select with whom to engage in interactions about reforms. They discuss the implications for scientific dialogue, and for informing educational policy studies and the practice of educational policy makers and school administrators. (DIPF/Orig.).
