Relational collaboration among spatial multipoint competitors

The presence of network ties between multipoint competitors is frequently assumed but rarely examined directly. The outcomes of multipoint competition, therefore, are better understood than their underlying relational mechanisms. Using original fieldwork and data that we have collected on an interorganizational network of patient transfer relations within a regional community of hospitals, we report and interpret estimates of Exponential Random Graph Models (ERGM) that specify the probability of observing network ties between organizations as a function of the degree of their spatial multipoint contact. We find that hospitals competing more intensely for patients across multiple geographical segments of their market (spatial multipoint competitors) are significantly more likely to collaborate. This conclusion is robust to alternative explanations for the formation of network ties based on organizational size differences, resource complementarities, performance differentials, and capacity constraints. We show that interorganizational networks between spatial multipoint competitors are characterized by clear tendencies toward clustering and a global core-periphery structure arising as consequences of multiple mechanisms of triadic closure operating simultaneously. We conclude that the effects of competition on the structure of interorganizational fields depends on how markets as physical and social settings are connected by cross-cutting network ties between competitors.     

Stakeholder influence,government power,and new opportunities for Chinese Internet companies: A multilevel network analysis of cross-sector networks

In recent decades, Chinese Internet companies have experienced exponential growth. As the Internet industry increasingly commends tremendous financial resources, they also face growing stakeholder expectations for corporate social responsibility (CSR) actions. One way through which Chinese Internet companies conduct CSR is by building cross-sectoral collaborations with nonprofit and nongovernmental organizations (NGOs) and governmental agencies. Aiming to understand Internet companies’ strategic relationship building on CSR issues, the researchers drew from stakeholder influence theory and research on a network approach to stakeholder influence, and applied multilevel network analysis to model three networks related to Chinese Internet companies’ CSR collaborations. Specifically, we found that power and urgency are significant predictors of the structure of Internet companies’ cross-sector CSR alliance network. Organizations affiliated or endorsed by the central Chinese government are the most desirable CSR stakeholders. Additionally, the study also revealed that for Internet companies, devoting their attention to Internet-related social issues could increase their desirability as strategic stakeholders from other sectors and among Internet companies.     

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.     

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.     

Social network boundaries and tricky to access populations: a qualitative approach

Social network researchers are routinely faced with the boundary specification problem, namely, where to place limits on nodes that constitute a social network. The complexity of the issue varies; some networks are easily delineated, while others are not. For instance, if we were interested in exploring the relations among older people in an elder care home, then ‘residence’ would define actor membership. However, where a network exists with ambiguous, perhaps unstable, outer limits and its population proves tricky to access, significant challenges arise for the researcher. In this article, I consider ‘the boundary specification problem’ as it relates to an amorphous, ‘tricky-to-access’ population of cross-community youth leaders in North Belfast, Northern Ireland. This article will review current strategies and approaches for bounding social networks, assess the applicability of each and provide practical insights and extended ideas around delineating ambiguous, unstable, elusive structures composed of ‘tricky to access’ populations.     

Social and semantic coevolution in knowledge networks

Socio-semantic networks involve agents creating and processing information: communities of scientists, software developers, wiki contributors and webloggers are, among others, examples of such knowledge networks. We aim at demonstrating that the dynamics of these communities can be adequately described as the coevolution of a social and a socio-semantic network. More precisely, we will first introduce a theoretical framework based on a social network and a socio-semantic network, i.e. an epistemic network featuring agents, concepts and links between agents and between agents and concepts. Adopting a relevant empirical protocol, we will then describe the joint dynamics of social and socio-semantic structures, at both macroscopic and microscopic scales, emphasizing the remarkable stability of these macroscopic properties in spite of a vivid local, agent-based network dynamics.     

Comparing the discussion networks and voluntary association memberships of immigrants and non-immigrants in U.S. suburban gateways

Using survey data from five Chicago (U.S.) suburbs, we build regression models comparing the social lives of immigrants and non-immigrants. We define immigration several ways (citizenship, legal status, immigrant generation, length of time in U.S., and race/ethnicity). Results indicate that the size, longevity and density of immigrants’ discussion networks are mostly comparable to those of non-immigrants, as are the number and longevity of their voluntary association memberships. Immigrants and non-immigrants differ little in geographic location of their network confidants and organizational memberships. However, there is less racial/ethnic variety in immigrants’ social lives, particularly if they are Latinx or not citizens.     

Detecting measurement bias in respondent reports of personal networks

Inaccuracy of sociometric reports poses a serious challenge to social network analysis. Nevertheless, researchers continue to draw potentially misleading conclusions from flawed data. We consider two particular types of systematic error in measurement of network size: individuals over/underreporting others (expansiveness bias), and individuals being over/underreported by others (attractiveness bias). We examine evidence of individual variation in these biases in one apparently typical sociometric dataset. We specifically suggest that variation in expansiveness bias may commonly distort findings concerning characteristics of individual networks (e.g. size, range, density), and properties of whole networks (e.g. inequality, transitivity, clustering, and blockmodels). We suggest methodological improvements and urge further research.     

Spatial distance and mobile business social network density

Following the increasing adoption of mobile communication, scholars have shown interest in the role of place on the structure of mobile social networks. The purpose of this study is to investigate the association between spatial distance and the closure and diversity of businesses mobile social networks. We used a database that aggregates actual mobile communication patterns of business users of a large Israeli cell phone company (n?= 16,199). Our findings, among a large sample of businesses, provide support for the place and mobile communication perspective. The results reveal a negative association between spatial distance and mobile business communication networks. As spatial distance between business network members increases, business social ties through mobile communication decreases. Furthermore, the results also revealed a negative association between spatial distance and mobile network density. As the spatial distance between business users increases, the density of the mobile communication network diminishes. Physical proximity promotes the development of dense business networks. The implications of the findings are discussed.     

Mapping networks in corporate social responsibility communication on social media: A new approach to exploring the influence of communication tactics on public responses

Grounded in the networked stakeholder management theory and two-way communication, this study provides a snapshot of networks between companies and publics on Twitter in the context of corporate social responsibility (CSR) communication. Results showed that CSR communication activities (i.e., informing, retweeting, and mentioning) empowered a corporation through centralizing its network position and gaining public support (i.e., emotional, influencer, and knowledge support). In addition, degree centrality mediated the relationship between corporate retweets and stakeholder support and between corporate response and stakeholder support.     

Network effects on organizational decision-making: Blended social mechanisms and IPO withdrawal

This paper develops a new approach to the study of network effects in organizations and markets by proposing that structural influences on social and economic action result from contingent blends of well-understood social mechanisms. We emphasize the interplay of three different network processes: resource and information transfer, status signaling and certification, and social influence. Different mixes of these mechanisms characterize disparate networks because the obligations imposed by ties and the capacities of partners result in situations where mechanisms amplify or diminish one another. We test hypotheses about mechanism interactions using four years (1997–2000) of data on high-technology IPOs that situate organizational decisions about whether to withdraw an offering in two distinct networks. We find that network mechanisms exert multiple moderating effects on one another and that those effects vary systematically across venture capital syndicate and director interlock networks. These findings help to explain why different networks exert disparate effects, why the effects of some structures change as their larger contexts shift, and why even very successful organizations can sometimes find themselves hamstrung by their connections.     

Reconsidering “community liberated”: How class and the national context shape personal support networks

The “community liberated” thesis has been influential in describing contemporary social support systems. Specifically, “community liberated” argues that people do not seek support in their immediate neighborhood but rather entertain a network of far-flung ties to support-providing alters. This paper uses personal network data from six countries – Australia, Germany, the US, Austria, Hungary and Italy – to evaluate this argument and shows that the degree of liberation of one's community is strongly linked to one's socioeconomic status – specifically, one's education level. Additionally, we describe strong country-level heterogeneity in the spatial dynamics of personal support networks and find national contexts to be moderating the effect of education on community liberation, especially in Italy and Hungary, thus suggesting network geographic dispersion to be linked to national economic structures and labor markets. The paper thus elucidates the effect of two different, yet related social contexts on personal networks: the class context and the national context.     

French connections: the networking strategies of French highly skilled migrants in London

Although the migration studies literature often takes social networks for granted, these social ties are not spontaneous but require effort and nurturing. There has been insufficient research on the actual process of networking, especially among highly skilled migrants. Our understanding of why and how migrants form networks with particular characteristics is still poor. In this article, we argue that it is necessary to consider both the structure and content of networks – the nature of the relationships as well as the flow of resources within various social ties. Drawing on qualitative data from a study of highly skilled French migrants in London's business and financial sector, we use a microanalysis of network‐making processes. In the context of London as a dynamic and highly competitive financial centre, we examine the importance of opportunities, skills and shared interests in building new social relationships from scratch. In addition, we also assess how mobility and proximity, virtual communication and co‐presence impact on geographically dispersed networks and why some long distance relationships endure while others fade over time. By bringing together classic literature on professional networking and wider discussions on how relationships are managed across time and space, our work contributes to a fuller understanding of why and how highly skilled migrants form networks with particular characteristics.     

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.     

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.     

Subject to Evaluation: How Parents Assess and Mobilize Information from Social Networks in School Choice

A rich literature examines how information spreads through social networks to influence life opportunities. However, receiving information does not guarantee its use in decision making. This article analyzes information evaluation as a fundamental component of social network mobilization. The case of school choice, where the value of information may be more uncertain, brings this evaluative dimension to the forefront. Interviews with 55 parents in Boston show how parents selecting schools assess their social network ties as information sources, privileging information from those they perceive to have affinity and authority. These evaluative criteria map onto disparate networks to engender unequal mobilization of this information. The findings illuminate mechanisms sustaining inequality in social network mobilization and reorient scholars to consider processes underlying information use alongside information diffusion to attain a more complete understanding of how network resources are mobilized in action.     

Who benefits from network intervention programs? TERGM analysis across ten Philippine low-income communities

Building social capital and strengthening social networks among members of low-income communities has been recommended as a potential pathway out of poverty. However, it is not clear how network-strengthening interventions and community-based programs interact with pre-existing networks and power structures. We examine the impact of one such intervention in ten low income communities in the Philippines. The intervention is a standardized program of a faith-based organization implemented in thousands of communities in multiple countries. It brings together low-income individuals in each community for 16 weekly sessions about health, income generation, and Christian values. An important but yet unmeasured goal of the intervention is the strengthening of social networks among the participants. We measured the social networks before and after the intervention and analysed their changes both separately and jointly for all ten communities with temporal exponential random graph models (TERGM). We modelled the post-intervention network structures conditioning on the pre-intervention networks, pre-intervention node attributes, and attribute changes through the intervention. We found social engagement (measured by social visits to others) to moderate most consistently the effects of the intervention across the ten communities. Those who were more socially engaged consistently strengthened their networks through the intervention. By contrast, some network mechanisms strongly diverged between the communities. In particular, religiosity was positively associated with gaining social links through this faith-based intervention in some communities and negatively in others. Similar communities may in some aspects react to the same intervention in opposite ways—a phenomenon that should be further explored through studies of larger numbers of comparable networks.     

Violence,Networks, and International Migration from Colombia

Although Colombia is a major country of emigration, little is known about its citizens' motivations for migration. Social and economic conditions have been studied as determinants of migration, but violence has received less attention. We examine how social networks and violence function to promote emigration from Colombia by linking event‐history data from the Latin American Migration Project to external data on violence and economic conditions. We show that emigration is more likely to be initiated by those with higher education, those with network connections to migrants, and during periods of greater violence and increased police presence. Although violence acts powerfully to determine when people migrate, the geographic distribution of social capital determines where they go. Not surprisingly, migrants go to locations where people in their social networks are currently living or have been earlier.     

SPATIAL COMPETITION AND THE PRICE OF COLLEGE

This article provides the first evidence that universities compete directly on price, and that the market for students depends on the proximity of competitors. Exploiting detailed data from private U.S. universities, price competition is tested by introducing geographic proximity into a spatial-autoregressive tuition model. Standard spatial models show that list and net tuition are inversely related to distance between institutions, consistent with price competition in higher education. An extension to the spatial-econometrics literature relaxes a constraint that estimated spatial relationships are common across all observations, implying that spatial effects differ across qualitative classes of institutions . ( JEL C21, I2, L11)