Emmanuel Lazega

Position in formal structure, personal characteristics and choices of advisors in a law firm: A logistic regression model for dyadic network data☆

This paper presents a statistical model for the analysis of binary sociometric choice data, the p2 model, which provides a flexible way for using explanatory variables to model network structure. It is applied to examine the influence of the formal structure of an organization on interactions among its members. It is shown to provide a general and precise method for addressing this substantive issue. We identify the respective effects of position in the formal structure (status, seniority, division of work and office membership) and selected personal characteristics of members of a corporate law firm on their choices of advisors. Flows of advice are shown to be consistently shaped by status games and the pecking order in the firm. Other dimensions help members in mitigating the effect of this strong rule. This approach ultimately provides more understanding of how members of such firms try to balance cooperation and competition in terms of access to and management of key resources.

Exponential random graph models for multilevel networks

a b s t r a c t Modern multilevel analysis, whereby outcomes of individuals within groups take into account group membership, has been accompanied by impressive theoretical development (e.g. Kozlowski and Klein, 2000) and sophisticated methodology (e.g. Snijders and Bosker, 2012). But typically the approach assumes that links between groups are non-existent, and interdependence among the individuals derives solely from common group membership. It is not plausible that such groups have no internal structure nor they have no links between each other. Networks provide a more complex representation of interdependence. Drawing on a small but crucial body of existing work, we present a general formulation of a multilevel network structure. We extend exponential random graph models (ERGMs) to multilevel networks, and investigate the properties of the proposed models using simulations which show that even very simple meso effects can create structure at one or both levels. We use an empirical example of a collaboration network about French cancer research elites and their affiliations (Lazega et al., 2006, 2008) to demon- strate that a full understanding of the network structure requires the cross-level parameters. We see these as the first steps in a full elaboration for general multilevel network analysis using ERGMs.

Conventions and Structures in Economic Organization

This book contributes to the current rapprochement between economics and sociology. It examines the fact that individuals use rules and interdependencies to forward their own interests, while living in social environments where everyone does the same. The authors argue that to construct durable organizations and viable markets, they need to be able to handle both. However, thus far, economists and sociologists have not been able to reconcile the relationship between these two types of constraints on economic activity.

Norms, status and the dynamics of advice networks: A case study

The issue of the influence of norms on behavior is as old as sociology itself. This paper explores the effect of normative homophily (i.e. “sharing the same normative choices”) on the evolution of the advice network among lay judges in a courthouse. (Blau, 1955) and (Blau, 1964) social exchange theory suggests that members select advisors based on the status of the advisor. Additional research shows that members of an organization use similarities with others in ascribed, achieved or inherited characteristics, as well as other kinds of ties, to mitigate the potentially negative effects of this strong status rule. We elaborate and test these theories using data on advisor choice in the Commercial Court of Paris. We use a jurisprudential case about unfair competition (material and “moral” damages), a case that we submitted to all the judges of this court, to test the effect of normative homophily on the selection of advisors, controlling for status effects. Normative homophily is measured by the extent to which two judges are equally “punitive” in awarding damages to plaintiffs. Statistical analyses combine longitudinal advice network data collected among the judges with their normative dispositions. Contrary to what could be expected from conventional sociological theories, we find no pure effect of normative homophily on the choice of advisors. In this case, therefore, sharing the same norms and values does not have, by itself, a mitigating effect and does not contribute to the evolution of the network. We argue that status effects, conformity and alignments on positions of opinion leaders in controversies still provide the best insights into the relationship between norms, structure and behavior.

A Spinning Top Model of Formal Organization and Informal Behavior: Dynamics of Advice Networks Among Judges in a Commercial Court

The longitudinal study of advice networks among 240 judges at the Commercial Court of Paris permits the examination of learning as an interactive process. We argue that a spinning top model is a useful heuristic for intra-organizational learning in dynamic advice networks. This model proposes that a stabilized elite preserves accumulated knowledge in a community that overall experiences high turnover and systematic job rotation, and hence runs the danger of inadequately sharing knowledge among its members. We test the model by analyzing the structure and dynamics of advice networks among judges at the Commercial Court of Paris. This dynamic structure reflects the informal homophilous preferences among judges organized in a strong formal system, a high relational turnover in the selection of advisors, and the emergence of an elite of senior advisors that stabilizes the learning process - much like the behavior of a spinning top. This case study also identifies an endogenous process of increasing and then decreasing centralization of this network over time, raising questions about the maintenance of the stability of the pecking order and about the relationship between learning and seniority. Results illustrate the importance of dynamic over static network analysis and call for a renewed attention to formal structure in organizations.

Multilevel Network Analysis for the Social Sciences: Theory, Methods and Applications

In this chapter we propose an integrated approach to study the effects of individual network position and global network structure on the attitudes and opinions of individuals within groups. The Multilevel Social Influence (MSI) model used here is generic, and can incorporate both individual network position measures such as centrality or brokerage position, as well as group level network properties such as the density, centralization or fragmentation of the network. This enables the possibility to disentangle and identify the relative importance of “individual social capital” from “collectively owned social capital”. Focusing on 201 employees in 27 teams as a specific empirical example, we demonstrated how employee job satisfaction can be explained by looking at the individual attributes of the employee, the position of the employee in the group’s trust network (indegree centrality), and the group structure (density and centralization).This volume provides new insights into the functioning of organizational, managerial and market societies. Multilevel analysis and social network analysis are described and the authors show how they can be combined in developing the theory, methods and empirical applications of the social sciences. This book maps out the development of multilevel reasoning and shows how it can explain behavior, through two different ways of contextualizing it. First, by identifying levels of influence on behavior and different aggregations of actors and behavior, and complex interactions between context and behavior. Second, by identifying different levels as truly different systems of agency: such levels of agency can be examined separately and jointly since the link between them is affiliation of members of one level to collective actors at the superior level. It is by combining these approaches that this work offers new insights. New case studies and datasets that explore new avenues of theorizing and new applications of methodology are presented. This book will be useful as a reference work for all social scientists, economists and historians who use network analyses and multilevel statistical analyses. Philosophers interested in the philosophy of science or epistemology will also find this book valuable. ​

Analyse de réseaux d'une organisation collégiale : les avocats d'affaires

Emmanuel Lazega : Analyse der Netzwerke einer kollegialen Organisation : die Wirtschaftsanwalte. ; ; Dieser Aufsatz identifiziert zwei Mechanismen der informellen Integration einer kollegialen Organisation. Der erste Mechanismus grundet auf den Zusammenhalt der Verbindungen zwischen den Mitgliedern. Dieser Zusammenhalt sichert eine informelle Integration da, wo Konflikte zwischen Beruflichen in der Organisation auftauchen konnten. Der zweite Mechanismus ist auf der strukturalen Gleichwertigkeit der Mitglieder begrundet. Diese Aquivalenz sichert eine informelle Integration, indem sie der Abhangigkeit der Beruflichen untereinander eine besondere Konfiguration gibt. Zusammenhalt und strukturale Gleichwertigkeit werden dank einer Untersuchung der Netzwerke zur Zusammenarbeit, zu gegenseitigen Ratschlagen und Freundschaften innerhalb einer amerikanischen Firma von Wirtschaftswanwalten gemessen. Das Vorhandensein dieser beiden Mechanismen wird wie eine Auswirkung der beruflichen Handlungslogik interpretiert, auf die Art, wie die kollegialen Organisationen sich strukturieren. Hieraus wird geschlossen, dass die Analyse der Netzwerke die Organisationsanalyse erneuern kann. Uber die einfache Beschreibung der Aktorenspiele und die Durchleuchtung der Strukturen hinaus, erlaubt sie formal die Art der Bindungen zwischen der Organisation und ihren sozialen Umfeld zu betrachten.

Bringing personalized ties back in : their added value for biotech entrepreneurs and venture capitalists interorganizational networks

Using a unique data set collected in France among biotech entrepreneurs and their venture capital investors (VCs), we measure the added value of personal relationships at the interorganizational level. Our analyses show that when two entrepreneurs share a personal collaboration tie or a personal friendship tie with a VC investor, the probability of having an advice tie and thus exchange tacit knowledge increases significantly. We confirm the importance of this kind of social embeddedness in the biotech industry where personalized ties—as opposed to institutional and contractual relationships—at the interorganizational level had not yet been examined systematically. Our results suggest that strategies of personalization of exchanges are vital for interorganizational learning. These strategies help entrepreneurs to access resources, participate in knowledge building, and co-orientate activities in this sector.

Embeddedness as a multilevel problem: A case study in economic sociology

Economic sociology has established the interdependencies between economic and social structures using the notion of embeddedness of the former in the latter. However research usually studies inter-organizational commercial networks and inter-individual informal networks separately. In this article we use a multilevel framework to analyze jointly economic networks between firms and informal networks between their members in order to reframe this embeddedness hypothesis. Based on a network study of a trade fair for television programmes in Eastern Europe we show that while each level has its own specific processes they are partly nested. Beyond this result, we observe that these levels of agency emerge in different contexts and in different temporalities. To conclude, we show that in order to understand performance on a market one needs to look at this dual positioning of individuals and organizations.

Social selection models for multilevel networks

Abstract Social selection models (SSMs) incorporate nodal attributes as explanatory covariates for modelling network ties ( Robins et al., 2001 ). The underlying assumption is that the social processes represented by the graph configurations without attributes are not homogenous, and the network heterogeneity maybe captured by nodal level exogenous covariates. In this article, we propose SSMs for multilevel networks as extensions to exponential random graph models (ERGMs) for multilevel networks ( Wang et al., 2013 ). We categorize the proposed model configurations by their similarities in interpretations arising from complex dependencies among ties within and across levels as well as the different types of nodal attributes. The features of the proposed models are illustrated using a network data set collected among French elite cancer researchers and their affiliated laboratories with attribute information about both researchers and laboratories ( Lazega et al., 2006 , Lazega et al., 2008 ). Comparisons between the models with and without nodal attributes highlight the importance of attribute effects across levels, where the attributes of nodes at one level affect the network structure at the other level.