Corporate competition: A self-organized network

A substantial number of studies have extended the work on universal properties in physical systems to complex networks in social, biological, and technological systems. In this paper, we present a complex networks perspective on interfirm organizational networks by mapping, analyzing and modeling the spatial structure of a large interfirm competition network across a variety of sectors and industries within the United States. We propose two micro-dynamic models that are able to reproduce empirically observed characteristics of competition networks as a natural outcome of a minimal set of general mechanisms governing the formation of competition networks. Both models, which utilize different approaches yet apply common principles to network formation give comparable results. There is an asymmetry between companies that are considered competitors, and companies that consider others as their competitors. All companies only consider a small number of other companies as competitors; however, there are a few companies that are considered as competitors by many others. Geographically, the density of corporate headquarters strongly correlates with local population density, and the probability two firms are competitors declines with geographic distance. We construct these properties by growing a corporate network with competitive links using random incorporations modulated by population density and geographic distance. Our new analysis, methodology and empirical results are relevant to various phenomena of social and market behavior, and have implications to research fields such as economic geography, economic sociology, and regional economic development.     

Old friends and new acquaintances: Tie formation mechanisms in an interorganizational network generated by employee mobility

This study investigates mechanisms of tie formation in an interorganizational network generated by the mobility of employees between organizations. We analyze a data set that contains information on all organizations in the Stockholm metropolitan area between 1990 and 2003. We show that the formation of new ties is contingent upon the direction of past ties, and that most connections occur at an intermediate geodesic distance of 2 and 3. The findings highlight the importance of tie direction and indirect connections in research on network dynamics and knowledge exchanges stemming from the mobility of employees across organizations.     

Effects of competition on collective learning in advice networks

This paper looks at the effect of identifying alters as direct competitors on their selection as advisors. We differentiate between two kinds of competition: cut-throat vs friendly. We argue that, unlike cut-throat competition, friendly competition makes collective learning possible as a social process: when knowledge is built in interactions that are able to mitigate the negative effects of status competition and take place in homophilous social niches; and when the quality of this knowledge is guaranteed by members with epistemic status in these niches. Social niches and status facilitate advice seeking and collective learning because advice seeking between direct competitors is not obvious even when members have a common interest in sharing advice – a learning-related dilemma of collective action. We apply this reasoning to a network dataset combining identification of direct competitors and selection of advisors among the elite of cancer researchers in France. We use a procedure of multiplex stochastic block-modeling designed by Barbillon et al. (2015) to measure the effect of these identifications of direct competitors on the structure of the advice network. Results obtained with this dataset support our theory.     

Residential mobility and the spatial dispersion of personal networks: Effects on social support

Using a representative national sample of personal networks, this article explores how the spatial dispersion of networks, residential mobility and social support are linked. Three issues will be addressed here. Firstly, how is the spatial dispersion of personal networks related to individuals’ social characteristics, network composition and residential mobility? Secondly, how do the spatial dispersion of networks, residential mobility and their combined effect influence the number and (thirdly) the structure of emotional support ties? Results showed that the extent of the support was affected neither by the geographical distribution of the networks nor by residential mobility. Living far from one's birthplace, however, exerted two distinct, and opposite effects on the support network structure. On the one hand, mobility led to high spatial dispersion of personal contacts, which in turn favored a sparsely knit network centered around the mobile individual. On the other hand, by controlling for the effect of distance between the contacts, we found that individuals that cited long-distance ties tended to be part of more transitive support networks than those that cited local ties. We interpreted the latter effect as evidence that transitive ties may survive greater spatial distances than intransitive ones. These findings are discussed in view of spatial mobility and social network research.     

Structure in context: A morphological view of whole network performance

Network perspectives in organizational research have focused primarily on how the embeddedness of actors shapes individual, or nodal, outcomes. Against this backdrop, a growing number of researchers have begun to adopt a wider lens on organizational networks, shifting the focus to collective, or whole network, performance. Yet, efforts to understand the relationship between whole network structure and whole network performance have produced conflicting findings, which suggests that a different approach may be needed. Drawing on macrostructural sociology, we propose a "whole network morphology" framework, which argues the whole network structure-performance relationship is contingent on other fundamental—relational and cultural—whole network dimensions. Subsequently, we undertake an application of our framework, through which we demonstrate how a morphological view helps address conflicting findings on the structure-performance relationship. We study 250 whole interorganizational networks known as Accountable Care Organizations (ACOs), which collectively comprise more than 44,000 healthcare organizations and 250,000 physicians. Consistent with previous work, we do not find a clear association between structural connectedness and performance. However, we find that a more disconnected network structure is associated with negative ACO performance when the relational strength of network ties is high. We also find evidence of better ACO performance in the presence of a physician cultural orientation when the whole network is more connected.     

Double-embeddedness: Spatial and relational contexts of tie persistence and re-formation

Personal relationships are embedded in both spatial and relational contexts. Using data on 60 intentional communities from the Urban Communes Data Set, we examine how such embedding is related to the persistence and re-formation of close personal ties over a thirteen year period, beginning from when most members had been out of their group environments more than a decade. We find that local network structure—the pattern of dyads immediately surrounding any dyad—is extremely weighty in which ties persist, which lapse, and which are re-initiated, but that the precise ways in which local structure affects contact are bound up with the distance between dyad members. We also find asymmetries in these processes that other studies have been unable to uncover—that processes that lead ties to be dropped are not the same as those that lead them to be renewed; that increases in local embeddedness are not opposite of decreases; that change in contact is not the same as change in friendship. Finally, there is evidence of hierarchical effects influencing the retention of friendships more than twenty-five years after most respondents left their groups.     

Extrapolative simulation of neighborhood networks based on population spatial distribution: Do they predict crime?

Objectives

Previous criminological scholarship has posited that network ties among neighborhood residents may impact crime rates, but has done little to consider the specific ways in which network structure may enhance or inhibit criminal activity. A lack of data on social ties has arguably led to this state of affairs. We propose to avoid this limitation by demonstrating a novel approach of extrapolatively simulating network ties and constructing structural network measures to assess their effect on neighborhood crime rates.

Methods

We first spatially locate the households of a city into their constituent blocks. Then, we employ spatial interaction functions based on prior empirical work and simulate a network of social ties among these residents. From this simulated network, we compute network statistics that more appropriately capture the notions of cohesion and information diffusion that underlie theories of networks and crime.

Results

We show that these network statistics are robust predictors of the levels of crime in five separate cities (above standard controls) at the very micro geographic level of blocks and block groups.

Conclusions

We conclude by considering extensions of the approach that account for homophily in the formation of network ties.     

The embeddedness of social relations in inter-firm competitive structures

Collaboration with direct competitors is a valuable, yet risky, strategy that might pave the way for competitive advantages. Thus far, we have lacked a detailed understanding of how competitive relations between firms shape collaborative relations between their managers, and consequently the structural interdependencies at both levels. The primary objective of this study is to understand the multi-level structure underlying the ambiguous relationship of coopetition, a state of simultaneous collaboration and competition. Hence, we investigate the influence of perceived inter-firm competition on interpersonal knowledge flows among managers within three high-tech clusters by applying multi-level exponential random graph models to network data. Our results reveal that the interpersonal exchange of advice and information depends strongly on the inter-firm competitive structure and that competitive structures shape these interpersonal exchanges in different ways. Thereby, our research demonstrates that managers utilize asymmetries by means of perceived competitive relations to seek work-related advice and information from their competitors and that stronger forms of competition are significant, but not insuperable, obstacles to the exchange of knowledge.     

Validation of tie corroboration and reported alter characteristics among a sample of young men who have sex with men

The differences between direct ties (i.e., ties between a respondent and their nominees) and ties between nominees (indirect) are key to understanding network structure, yet remain understudied. Within a sample of 175 young men who have sex with men, we explored the corroboration of sex and drug ties, and factors associated with corroboration. The majority of instances in which there was no corroboration for either sex or drug ties was due to one individual not appearing in another respondent’s network. When an individual did appear in another respondent’s network, direct sex and drug ties were corroborated in most cases. We also found that more indirect sex ties were corroborated than direct sex ties (95.7% vs. 88.9%), but the reverse was true for indirect versus direct drug ties (73.1% vs. 84.1%). Strength of relationship and frequency of communication were both associated with confirmed direct ties, but not with indirect ties. Based on these findings, we recommend that direct and indirect ties be treated differently in network analyses.     

Analysing networks of networks

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.     

Whom do we lose? The case of dissimilarity in personal networks

Previous research finds that individuals tend to form ties with similar others much more often than with dissimilar others. However, we know relatively little about tie loss and to what extent this is driven by (dis)similarity. In this paper, we argue that ties to persons who are dissimilar with regard to gender, age, ethnicity, and education are lost faster than ties to similar persons – and we test three explanations for this faster decay of ties with dissimilar others: lack of meeting opportunities, preferences for similarity, and lower network embeddedness. To test these explanations, we analysed two waves of the Survey on the Social Networks of the Dutch (SSND, 2007, 2014). These SSND-waves contain comprehensive longitudinal panel data on ego networks of 441 respondents, who were interviewed about a wide range of relationships, their alters’ sociodemographic characteristics, where and when they met their alters, as well as how and whether they maintain these relationships. We modelled tie loss by event history analyses. Results show that ties to persons who were dissimilar are more likely to be lost faster, and that tie loss occurs mostly in the early years of a relationship. However, meeting opportunities, preferences for similarity, and network embeddedness are unable to explain why ties to dissimilar others are lost faster. We conclude that dissimilarity is a powerful driver of tie loss, and that more arguments and research are needed to fully understand the underlying mechanisms.     

Cultural differences in an interorganizational network: Shared public relations firms among Japanese and American companies

This article investigates the impact of national culture on interorganizational relationships among the organizations. Thirty-five companies which have stock offerings in U.S (17 Japanese and 18 American) were matched by their business types. The interorganizational network was defined as the number of shared public relations firms among the companies.A network analysis revealed that the network of shared public relations firms was loosely connected and American companies were more central. The companies were generally clustered into two groups, one composed of the Japanese companies and another of the American companies. However, the results also showed that some companies that had the same business type were more tightly clustered than others within the group. These results indicated that the network structure of shared public relations firms was influenced by the differences in national cultures as well as the companies' business types.     

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.     

Work ties beget community? Assessing interactions among transnational private governance organizations in sustainable agriculture

Transnational private governance organizations (TPGOs) set standards for various sustainability issues and industries. TPGOs individually address only facets of larger problems such as land conversion, and competition among TPGOs also affects TPGO governance, meaning coordination across their activities can be required. Studies have shown that ties between TPGOs and other organizations facilitate information sharing, coordination and socialization. A systematic analysis of the structure of such ties, however, is lacking. Using the employment ties of staff and governors, we analyse the inter‐organizational communities of TPGOs that focus on agriculture. Our findings cast doubt on the existence of a coherent community of sustainable agricultural TPGOs: very few communities involve multiple TPGOs. While TPGOs with a similar product focus may form links with similar organizations, there are few direct and indirect ties among TPGOs. Researchers should therefore be cautious about assuming that a TPGO policy community crossing organizational and policy divides currently exists.     

Social fabric and fear of crime: Considering spatial location and time of day

Criminologists have long noted that social networks play a role in influencing residents' fear of crime, but findings vis a vis the exact nature of that role have been mixed. More social ties may be associated with less fear of crime through their role in collective action, trust, and emotional support, but also with more fear of crime because of their role in the diffusion of information on local crime patterns. In what follows, we suggest temporal and spatial distinctions in how social ties matter for fear of crime with respect to these different mechanisms. Analysis of data from a large scale egocentric network study in Southern California provides evidence for these claims.     

What makes a network go round? Exploring the structure of a strong component with exponential random graph models

A strong component is a subgraph in a directed network where, following the direction of ties, all nodes in the graph are reachable from one another. Mutual reachability implies that every node in the graph is theoretically able to send materials to and/or influence every other node suggesting that strong components are amongst the more egalitarian network structures. Despite this intriguing feature, they remain understudied. Using exponential random graph models (ERGM) for directed networks, we investigate the social and structural processes underlying the generation of strong components. We illustrate our argument using a network of 301 nodes and 703 personal lending ties from Renaissance Florence. ERGM shows that our strong component arises from triadic clustering alongside an absence of higher-order star structures. We contend that these processes produce a strong component with a hierarchical, rather than an egalitarian structure: while some nodes are deeply embedded in a dense network of exchange, the involvement of others is more tenuous. More generally, we argue that such tiered core-periphery strong components will predominate in networks where the social context creates conditions for an absence of preferential attachment alongside the presence of localized closure. Although disparate social processes can give rise to hierarchical strong components linked to these two structural mechanisms, in Florence they are associated with the presence of multiple dimensions of social status and the connectedness of participants across disparate network domains.     

The analysis of multiple ties in longitudinal egocentric network data: A case study on bidirectional relationships between trust and drug use

We extend multi-level models to examine single egocentric network ties to the joint analysis of paired dynamic ties. Two analytic challenges are addressed. First, inference needs to account for multiple layers of nesting: ties are nested within pairs, pairs are nested within time points, and time points are nested within egos. Second, the focus is on the relationship between two dynamic ties; specification of outcome and predictor may be difficult. Instead, we treat both ties as outcomes. Our approach is used to analyze trust and reported drug use between egos and alters over time in a Bayesian framework.     

Interlocks in the Netherlands: Stability and careers in the period 1960–1980

The article is oriented towards the dynamics behind the network of interlocking directorates. In the interorganizational perspective the stability of different types of interlocks/lines between corporations is of strategic importance; in the intraclass perspective, the carriers of persons. The article therefore analyzes both. It shows that particularly primary interlocks (interlocks between the corporation in which the multiple director has an inside position and the other corporations in which he has outside positions) as well as multiple interlocks between corporations are considerably more stable than other interlocks. They are most likely being used for interorganizational purposes. Most of the interlocks however are generated by a very specific career pattern in which an executive position in a large corporation is almost a prerequisite to obtain many directorships.     

Missing data in networks: exponential random graph (p1) models for networks with non-respondents

Survey studies of complete social networks often involve non-respondents, whereby certain people within the “boundary” of a network do not complete a sociometric questionnaire—either by their own choice or by the design of the study—yet are still nominated by other respondents as network partners. We develop exponential random graph (p¹) models for network data with non-respondents. We model respondents and non-respondents as two different types of nodes, distinguishing ties between respondents from ties that link respondents to non-respondents. Moreover, if we assume that the non-respondents are missing at random, we invoke homogeneity across certain network configurations to infer effects as applicable to the entire set of network actors. Using an example from a well-known network dataset, we show that treating a sizeable proportion of nodes as non-respondents may still result in estimates, and inferences about structural effects, consistent with those for the entire network.If, on the other hand, the principal research focus is on the respondent-only structure, with non-respondents clearly not missing at random, we incorporate the information about ties to non-respondents as exogenous. We illustrate this model with an example of a network within and between organizational departments. Because in this second class of models the number of non-respondents may be large, values of parameter estimates may not be directly comparable to those for models that exclude non-respondents. In the context of discussing recent technical developments in exponential random graph models, we present a heuristic method based on pseudo-likelihood estimation to infer whether certain structural effects may contribute substantially to the predictive capacity of a model, thereby enabling comparisons of important effects between models with differently sized node sets.     

Change in Covert Social Movement Networks: The ‘Inner Circle’ of the Provisional Irish Republican Army

In this paper, we contribute to both the growing body of literature on social movement networks and the growing body of literature on change in networks by exploring patterns and mechanisms of change within the network of the ‘inner circle’ of the Provisional Irish Republican Army. Specifically, we focus upon the period between 1969 (when it formed) and 1988 (the last point for which we have been able to gather good data). Our primary aims are substantive. We want to know how this network changed over time. In addition, however, our analysis identifies changes which other analysts might look for in their networks and offers methodological suggestions for those who, like us, find that their networks do not meet the assumptions of mainstream approaches to modelling network dynamics. There is a further dimension to the paper, however. We are studying a covert social movement network. This is a special type of movement network whose organisation and dynamics are predicted to vary from other movement networks. Some have suggested that they are inclined to be relatively static because the need for trust within them is so great and the risk to whatever they hold secret so considerable when new ties are formed that their members tend only to recruit within the pool of their pre-existing ties and actively seek to minimise recruitment and the formation of new ties. One of the aims of our paper was to determine whether and to what extent this is so.