David Melamed

Forms of Exchange and Integrative Bonds Effects of History and Embeddedness

In this study we bring together two sociological traditions: experimental research on how different forms of exchange affect attachments to partners and relationships, and organizational research in natural settings on how embeddedness contributes to social capital. We conceptualize embeddedness in terms of the underlying forms of exchange—negotiated and reciprocal—that are associated with economic exchanges and the social relationships in which they are embedded. Building upon the reciprocity theory of social exchange, we test predictions of how relationship histories (i.e., different sequences of the two forms of exchange) and relationship contexts (i.e., embedding one form of exchange within an ongoing relation of the other form) modify effects of each form in isolation. Results from two experiments show that the reciprocal form of exchange, independent of close ties or personal associations, is critical for producing the strong trust and affective bonds typically associated with embedded relationships. A history or context of reciprocal exchange significantly boosts integrative bonds for negotiated exchange, whereas a history or context of negotiated exchange dampens integrative bonds for reciprocal exchange only moderately. The relative effects of history and context vary by actors’ positions of power.

Community Structures in Bipartite Networks: A Dual-Projection Approach

Identifying communities or clusters in networked systems has received much attention across the physical and social sciences. Most of this work focuses on single layer or one-mode networks, including social networks between people or hyperlinks between websites. Multilayer or multi-mode networks, such as affiliation networks linking people to organizations, receive much less attention in this literature. Common strategies for discovering the community structure of multi-mode networks identify the communities of each mode simultaneously. Here I show that this combined approach is ineffective at discovering community structures when there are an unequal number of communities between the modes of a multi-mode network. I propose a dual-projection alternative for detecting communities in multi-mode networks that overcomes this shortcoming. The evaluation of synthetic networks with known community structures reveals that the dual-projection approach outperforms the combined approach when there are a different number of communities in the various modes. At the same time, results show that the dual-projection approach is as effective as the combined strategy when the number of communities is the same between the modes.

Strong ties promote the evolution of cooperation in dynamic networks

Abstract Research on the evolution of cooperation in networked populations has assumed that ties are simply present or absent. Here we bring relational sociological insights about the strength of ties to bear on the problem of cooperation in dynamic networks. We argue that the value of ties affects their strength, which in turn promotes cooperation. We evaluate this argument with two studies. First, results from an agent-based model are consistent with the logic of our argument and are robust across a variety of initial conditions. Second, results from a controlled laboratory experiment with human participants support the key predictions. Across both studies we demonstrate that tie strength, operationalized as relationship duration, mediates the impact of tie value on cooperation.

Graded Status Characteristics and Expectation States

In this chapter, I detail a procedure for incorporating status characteristics with more than two ordered states, or graded status characteristics, into status characteristics theory. I revise theoretical definitions and assumptions accordingly. The new procedure adds a weighting function to the existing mathematical structure of the theory. I show that estimates using the new procedure are consistent with findings drawn from four experiments that were run in the standard setting for status characteristics theory. I also show that the procedure explains more variation and improves global model fit when predicting expectations using newly collected vignette data. I conclude with a general discussion of the procedures and implications for other expectation states theories and for research conducted outside the laboratory.

Behavioral Consequences of Embeddedness: Effects of the Underlying Forms of Exchange.

Network structures have strong effects on the frequency and terms of negotiated exchanges, shaping who exchanges with whom and who fares better or worse. In this study we ask how exchange patterns of commitment and inequality are affected when negotiated exchanges are combined with reciprocal exchanges in more complex relationships of embeddedness. Drawing on the organizational literature on embeddedness and economic performance and the social exchange literature on forms of exchange and integrative bonds, we predict that embedding negotiated exchanges in a relationship of reciprocal exchange will increase the strength of behavioral commitments and reduce the effects of structural power differences on inequality. We base these predictions on two theoretical mechanisms that link forms of exchange to bonds of attachment: the relational climate of trust and solidarity created by reciprocal exchange, and the more cooperative task structure of negotiated exchange. A laboratory experiment provides substantial support for our logic and predictions.

Comparative configurational analysis as a two-mode network problem: A study of terrorist group engagement in the drug trade

Abstract We generalize a form of two-mode network analysis to make it applicable to a cases-by-variables data format, and apply our approach for the study of terrorist group engagement in the drug trade, emphasizing the implications of our approach for policy in a study of 395 terrorist organizations. Based on the organizations’ levels of resources, network connectivity to other groups, ideological emphasis, and participation in multiple illicit economies, we identify several distinctive configurations of factors that lead to multiple types of drug activity. We also demonstrate a technique for assessing sampling variability in configurational models.

Do magnitudes of difference on status characteristics matter for small group inequalities

The theory of status characteristics and expectation states (SCT) explains how macro-level dimensions of stratification and specific abilities come to organize small group processes. The theory argues that people generate expectation states for each other based on relative standings on dimensions of stratification such that people with the more culturally valued states of the characteristics have higher expectations. Subsequently social influence, participation rates and evaluations of participation are purported to be directly related to expectation states. The result of this process is that large-scale inequalities are perpetuated in small group interactions, and individuals higher on abilities receive systematic advantages in small groups. SCT has received substantial experimental support for over 40years. However, the theory assumes that only states of relatively high and relatively low matter. That is, the theory and its applications assume that the magnitude of difference separating individuals on a dimension of stratification or ability is irrelevant. Recently, though, extensions to both the theory and its mathematics have been introduced that allow the magnitude of difference to be incorporated into the theorys predictions, supposedly yielding more precise predictions. This paper offers an experimental test of these procedures, showing that including the magnitude of difference into the theoretical predictions yields more precise estimates that explain more status-based inequalities.

Referent Networks and Distributive Justice

Abstract Purpose This study examines how the structure of referent networks, or the social network defined by knowing others’ reward levels, affects perceptions of distributive justice. The homogeneity of rewards in the referent network, the amount of inequality in the referent network, and an individual’s reward level are all associated with distributive justice perceptions. Several moderating relationships are also examined. Methodology/Approach We relied on data from a controlled laboratory experiment to test a series of theoretically derived hypotheses. Findings The study shows that several aspects about the structure of the referent network are important for shaping perceptions of distributive justice. Specifically, the reward heterogeneity and amount of inequality in the network are found to be negatively associated with distributive justice, while reward levels are found to be positively associated with distributive justice. Furthermore, the effect of reward levels on distributive justice is moderated by both (i) the presence of a referential standard for rewards and (ii) the amount of inequality in the network. Research Limitations/Implications While being among the first studies to demonstrate effects of referent networks on perceptions of fairness, it is unclear how group memberships combine with referent network effects and which factors may blur these relationships in uncontrolled environments. Subsequent scholarship on the effect of referent networks on justice perceptions should leverage multiple data sources. Originality/Value of Chapter Research on the effects of referents on justice perceptions has focused on particular referent individuals. We recast this issue in terms of referent networks, which highlights the empirical reality that individuals have a variety of sources or alters which could operate as referents.

The Duality of Organizations and their Attributes: Turning Regression Modeling “Inside Out”

We reformulate regression modeling so that ideas often associated with field theory and social network analysis can be brought to bear at every stage in the computation and interpretation of regression coefficients in studies of organizations. Rather than “transcending” general linear reality, we seek to get more out of it. We formulate a dual to regression modeling based on using the variables to learn about the cases. We illustrate our ideas by applying the new approach to a database of hundreds of violent extremist organizations, focusing on understanding which groups use or pursue unconventional weapons (chemical, biological, radiological, nuclear).

The Duality of Clusters and Statistical Interactions

We contend that clusters of cases co-constitute statistical interactions among variables. Interactions among variables imply clusters of cases within which statistical effects differ. Regression coefficients may be productively viewed as sums across clusters of cases, and in this sense regression coefficients may be said to be “composed” of clusters of cases. We explicate a four-step procedure that discovers interaction effects based on clusters of cases in the data matrix, hence aiding in inductive model specification. We illustrate with two examples. One is a reanalysis of data from a published study of the effect of social welfare policy extensiveness on poverty rates across 15 countries. The second uses General Social Survey data to predict four different dimensions of ego-network homophily. We find support for our contention that clusters of the rows of a data matrix may be exploited to discover statistical interactions among variables that improve model fit.