For the greater good: business networks and business social responsibility to communities

Business networks (co-operative arrangements between independent business organizations) may be the signature organizational form of the contemporary global economy. Many policy-makers and local leaders advocate business network membership as an alternative development strategy for regional economic vitality. The extant literature on business networks has focused on their association with business success. However, little is known about their impact on other aspects of community life. The purpose of this paper is to elaborate the role of network membership on one non-economic dimension of the business community interface. We examined the relationship between business network membership and business social responsibility to communities, defined as the provision of leadership and support for community betterment projects. Data were gathered from telephone interviews with a random sample of 460 non-metro small business operators in the USA. Independent t-tests and ordinary least squares regression analyses controlling for theoretically important variables were conducted. Findings show that networked businesses provide more leadership and support for their communities than non-networked businesses. However, networked businesses were no more likely than non-networked businesses to use local suppliers of goods and services.     

The Naming Game in social networks: community formation and consensus engineering

We study the dynamics of the Naming Game (Baronchelli et al. in J Stat Mech Theory Exp P06014, 2006b) in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.     

Comparative evaluation of community-aware centrality measures

Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not take into account the network community structure. Recently, a growing number of researches have shifted to community-aware centrality measures. Indeed, it is a ubiquitous feature in a vast majority of real-world networks. In the literature, the focus is on designing community-aware centrality measures. However, up to now, there is no systematic evaluation of their effectiveness. This study fills this gap. It allows answering which community-aware centrality measure should be used in practical situations. We investigate seven influential community-aware centrality measures in an epidemic spreading process scenario using the Susceptible–Infected–Recovered model on a set of fifteen real-world networks. Results show that generally, the correlation between community-aware centrality measures is low. Furthermore, in a multiple-spreader problem, when resources are available, targeting distant hubs using Modularity Vitality is more effective. However, with limited resources, diffusion expands better through bridges, especially in networks with a medium or strong community structure.

     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

Identifying influential user communities on the social network

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4–11.8% accuracy improvement over the state-of-the-art method.     

A dynamic perspective: local interactions driving the spread of social networks

ABSTRACT

This study takes a micro standpoint to quantitatively verify the extent to which local interactions affect the expansion of dynamic social networks. By reviewing and synthesizing the theoretical literature in this article, we seek to establish a pragmatic analytical framework to capture the underlying functioning of local interactions. It then aims to produce findings on seven distinct marketing campaigns in which the real-world virtual communities offer intact time-series connections. In our considered cases, the firm-to-consumer interactivity plays a pivotal role in social media marketing while the customer-driven influence advances individual information transmission. The significant positive relationships demonstrate features of interactivity within subgroups including reciprocal edges and directed 3-cliques associated with the evolution of the virtual community by electronic word-of-mouth (WOM) of both firm-to-consumer and consumer-to-consumer interactions. More importantly, the attenuation of local interactions to a large extent leads to the cut-off point of the growth of the dynamic social network.     

Contagion of network products in small-world networks

We formulate a model in which agents embedded in an exogenous social network decide whether to adopt a new network product or not. In the theoretical part of the paper, we characterize the stochastically stable equilibria for complete networks and cycles. For an arbitrary network structure, we develop a novel graph decomposition method to characterize the set of recurrent communication states, which is a superset of stochastically stable equilibria of the adoption game presented in our model. In the simulation part, we study the contagion process of a network product in small-world networks that systematically represent social networks. We simulate a generalization of the Morris (Rev Econ Stud 67(1):57–78, 2000) Contagion model that can explain the chasm between early adopters and early majority. Our numerical analysis shows that the failure of a new network product is less likely in a highly cliquish network. In addition, the contagion process reaches to steady state faster in random networks than in highly cliquish networks. It turns out that marketers should work with mixed marketing strategies, which will result in a full contagion of a network product and faster contagion rates with a higher probability.

     

Collaboration,Co-Production,Networks: Convergence of theories

Abstract

This article suggests a framework to study service delivery networks that draws on the theories of collaboration, co-production, and networks combined. We introduce four dimensions of co-production under ‘coproduction-oriented collaborations’. This framework allows us to ‘zoom in and zoom out’ when we study networks. Using the case method approach, the framework is applied to analyse four networks in Singapore. Findings suggest that network process, network structure, and characteristics of actors are crucial to a network’s performance and coproduction’s effectiveness. This article also offers implications for practice that in certain contexts the usage of these concepts is for managerial effectiveness and not for enhancing democratic values.     

Method towards discovering potential opportunity information during cross-organisational business processes using role identification analysis within complex social network

ABSTRACT

The key to discover potential opportunity information in cross-organisation business processes (COBPs) is to identify the primary roles and actors, i.e. how to obtain their associations according to the interactive behaviours within the complex social networks. The information of roles in COBPs is commonly considered important and explicitly related with activities contained in COBPs. In this paper, we define a role as a configurable resource model integrating the capabilities and knowledge required to the qualified actors. Furthermore, we introduce two networks named as role-based interactive behaviour network and handover of work social network to investigate the information on roles. How to build the complex social network mapped on roles from COBPs is also discussed, and an approach to obtain the potential opportunity information is proposed by combining with the significance of roles and actors. The simulation result shows that the primary roles may not completely correspond to the central position in networks, but they are closely associated with more reliable actors.     

Networks in Public Administration: Current scholarship in review

Abstract

Network-focused research in public administration has expanded rapidly over the past two decades. This rapid growth has created come confusion about terminology and approaches to research in the field. We organize the network literature in public administration using compact citation networks to identify coherent subdomains focused on (1) policy formation, (2) governance and (3) policy implementation. We trace how these domains differ in their approach to defining the role of networks, relationships and actors and to what extent the articles apply formal network analysis techniques. Based on a subsequent content analysis of the sample articles, we identify promising research avenues focused on the wider adoption of methods derived from social network analysis and the conditions under which networks actually deliver improved results.     

A Social Interactions Model with Endogenous Friendship Formation and Selectivity

This paper analyzes the endogeneity bias problem caused by associations of members within a network when the spatial autoregressive (SAR) model is used to study social interactions. When there are unobserved factors that affect both friendship decisions and economic outcomes, the spatial weight matrix (sociomatrix; adjacency matrix) in the SAR model, which represents the structure of a friendship network, might correlate with the disturbance term of the model, and consequently result in an endogenous selection problem in the outcomes. We consider this problem of selection bias with a modeling approach. In this approach, a statistical network model is adopted to explain the endogenous network formation process. By specifying unobserved components in both the network model and the SAR model, we capture the correlation between the processes of network and outcome formation, and propose a proper estimation procedure for the system. We demonstrate that the estimation of this system can be effectively done by using the Bayesian method. We provide a Monte Carlo experiment and an empirical application of this modeling approach on the friendship networks of high school students and their interactions on academic performance in the Add Health data. Copyright © 2015 John Wiley & Sons, Ltd.     

The effect of social distancing on the reach of an epidemic in social networks

How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

     

From network ties to network structures: Exponential Random Graph Models of interorganizational relations

Theoretical accounts of network ties between organizations emphasize the interdependence of individual intentions, opportunities, and actions embedded in local configurations of network ties. These accounts are at odds with empirical models based on assumptions of independence between network ties. As a result, the relation between models for network ties and the observed network structure of interorganizational fields is problematic. Using original fieldwork and data that we have collected on collaborative network ties within a regional community of hospital organizations we estimate newly developed specifications of Exponential Random Graph Models (ERGM) that help to narrow the gap between theories and empirical models of interorganizational networks. After controlling for the main factors known to affect partner selection decisions, full models in which local dependencies between network ties are appropriately specified outperform restricted models in which such dependencies are left unspecified and only controlled for statistically. We use computational methods to show that networks based on empirical estimates produced by models accounting for local network dependencies reproduce with accuracy salient features of the global network structure that was actually observed. We show that models based on assumptions of independence between network ties do not. The results of the study suggest that mechanisms behind the formation of network ties between organizations are local, but their specification and identification depends on an accurate characterization of network structure. We discuss the implications of this view for current research on interorganizational networks, communities, and fields.     

A voted based random forests algorithm for smart grid distribution network faults prediction

ABSTRACT

In this paper, we focus on fault prediction in the smart distribution network. modified version of voted random forest algorithm (VRF) is proposed for enhancing the predicting accuracy of the faults. We change the decision process by redesigning the voting algorithm by introducing multiple SVM models for voting model training. Based on the trained models, a simple NSGA algorithm is applied to find the best voting model. Results showed that the new algorithm could improve the accuracy and recall rate of the fault prediction, especially for the recall rate of the negative samples.     

Generating price‐effective intermodal routes

We describe the problem of intermodal international freight transport that is faced by some logistic service providers and the solution that ORTEC's logistic suite offers to these customers. This solution is based on a state of the art k‐shortest path algorithm for directed networks. Our focus is on the modeling aspects of the problem. In particular, we propose an approach in two phases and methods to reduce the size of the network, so as to obtain a solution within an acceptable calculation time.     

From agent-based modeling to actor-based reactive systems in the analysis of financial networks

We present a new framework for the analysis of financial networks, called Actor-based Reactive Systems (ARS), that pushes further the Agent-Based approach (ABM) by resorting to ideas coming from the study of distributed systems in computer science. Two distinctive features, namely a fundamentally different management of time and a fully decentralized control logic, have a profound impact in terms of expressiveness of analysis, flexibility of modeling, and efficiency of experimentation. To illustrate the feasibility of the framework, we develop a realistic case study by analyzing the systemic risk of a model of the European banking network with a nontrivial contagion procedure, that combines an initial asset shock with the negative feedback loop triggered by asset fire sales. We show that, compared to ABMs, ARSs bring about finer-grained analyses, with a greater degree of heterogeneity and adaptivity of economic agents. Moreover, the very low computational cost and the detailed account of the system’s execution support the design and the development of very flexible stress tests to rapidly experiment with many hypothetical scenarios in a test-oriented style.

     

Balancing on a tightrope: Managing the boundaries of a firm-sponsored OSS community and its impact on innovation and absorptive capacity

Realizing the innovation potential of OSS communities, firms now create or sponsor their own open source software (OSS) communities, generally as part of an open innovation strategy. However, maximizing the innovation capability of a sponsored OSS community is a challenging task since firms cannot rely on traditional hierarchical authority to control community members. Furthermore, a firm's efforts to manage its sponsored community may also impact the firm's absorptive capacity, or its ability to effectively absorb and leverage the valuable knowledge created by the community. Thus, the purpose of this article is to investigate two research questions: 1) How does the boundary management of a firm-sponsored OSS community impact the community's innovation capacity? and 2) How does the boundary management of a firm-sponsored OSS community impact the firm's absorptive capacity? Using the results from our qualitative analysis of eZ Systems and its successfully sponsored OSS community – eZ Publish – we develop a theoretical model depicting how the boundary management of a firm-sponsored OSS community influences both the community's innovation capacity and the absorptive capacity of the firm. In addition, the results of our study highlight the central importance of an integrative IT platform in boundary management activities.     

An improved spanning tree approach for the reliability analysis of supply chain collaborative network

A higher degree of reliability in the collaborative network can increase the competitiveness and performance of an entire supply chain. As supply chain networks grow more complex, the consequences of unreliable behaviour become increasingly severe in terms of cost, effort and time. Moreover, it is computationally difficult to calculate the network reliability of a Non-deterministic Polynomial-time hard (NP-hard) all-terminal network using state enumeration, as this may require a huge number of iterations for topology optimisation. Therefore, this paper proposes an alternative approach of an improved spanning tree for reliability analysis to help effectively evaluate and analyse the reliability of collaborative networks in supply chains and reduce the comparative computational complexity of algorithms. Set theory is employed to evaluate and model the all-terminal reliability of the improved spanning tree algorithm and present a case study of a supply chain used in lamp production to illustrate the application of the proposed approach.     

Community income distributions in a metropolitan area

We extend de Bartolome and Ross [C.A.M. de Bartolome, S.L. Ross, Equilibrium with local governments and commuting: Income sorting vs. income mixing, Journal of Urban Economics 54 (2003) 1–20] to the case when the income distribution in the metropolitan area is a continuous distribution. In particular, we consider a circular central city surrounded by a suburban community. All households must commute to the metropolitan center and public service levels differ in the two jurisdictions. There is intra-jurisdictional and inter-jurisdictional capitalization. Our model has an equilibrium in which the income distributions of the central city and of the suburban community do overlap. Our finding contrasts with the traditional finding of Alonso–Mills–Muth-type models of spatial sorting and of Tiebout-type models of fiscal sorting, both of which have been shown to predict that the income distributions of the two communities do not overlap. In addition, the model explains the fixedness in jurisdictional boundaries.     

Structural embeddedness and organizational social outcomes in a centrally governed mental health services network

Abstract

This article is an examination of the effects of network involvement, or structural embeddedness, on three organizationally based social outcomes. Specifically, we argue that in centralized, publicly funded networks an organization's structural embeddedness will be related to its trustworthiness, reputation and influence, as rated by other network members. Results from a network survey of a publicly funded health and human service network generally confirm our hypotheses and suggest additional work on the topic.