Social Capital in Network Organizations

In a dynamic network organization, member agents usually interact to coordinate their actions and to cooperate towards a common goal with which they have no previous experience. These relations allow them to produce a cohesive group to build and maintain their network. This paper will outline the effect of social capital on a network structure inside a network organization. There is increasing interest in cross-organization, serviceoriented computing that aims to dynamically amalgamate computational resources across organizational boundaries. The most common examples of these are the semantic web, cloud computing, crowdsourcing, and peer-to-peer networking technologies. A feature of all of these systems is that the organization’s services from data and software will be remotely invoked with other organizations’ services to be used together toward some common goal. In a cloud computing scenario, for instance, an agent may invoke data hosted by one organization with a remote application server for data mining run by another, and then store the result on a public cloud data service. This may often allow a number of competing service providers to achieve their respective service goal. Nevertheless, the multi-organizational nature of these systems means that they will invariably create uncertainty surrounding the capabilities and incentives of the organizations offering these services. Social Capital (SC) transcends issues surrounding heterogeneity of agent affiliations since it captures benefits resulting from the preferential treatment and collaboration among agents. Quantities of SC can be used to replace quantification of interpersonal trust among agents. When a network organization generates positive SC, constituent agents gain benevolence and behave in a cohesive manner (Smith, Giraud-Carrier, and Purser 2009). A main concern of multiagent systems is the coordination of autonomous agents that interact dynamically to achieve their goals. There is a need to develop SC assessment models or mechanisms that can measure qualities among autonomous agents operating in largescale and open service-oriented organizations. Such mechanisms are required to estimate the future behavior of agents and agents’ peers in order to simplify the interaction process Copyright c © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. with those peers. We are striving to overcome these problems with a novel perspective by utilizing a network organization (NO). This wide coverage collaboratively generated organization maintains by a disparate group of agents to decide and act locally. These agents share affinities and possess similar objectives or ideologies that are socially connected via social online mediums, such as Twitter and Facebook. Social Capital Measurements Social capital in the network can be defined as the cooperative nature of agents who belong to myriad networked organizations. These agents operate independently yet in unison from their organizational origins when they perform actions together. In other words, it can be characterized as the collocated or virtual collaboration to produce successful outcomes and connections (Alqithami and Hexmoor 2012). There are two major perspectives on SC in networks. In the macroscopic perspective, SC for the entire network is considered. In this view, individuals do not incrementally add to the system or withdraw units of SC. Instead the foci are on the system principles like norms and conventions that provide resources for overall social welfare. In contrast, the microscopic perspective adopted here explores how individuals can gain access to resources by their positions and connections in the network. There are a few main elements of the SC, which have a proportional relationship to one another. Topologically speaking, high bonding rates provide more opportunities for interaction and growth of social capital. However, network structure by itself is inadequate for determination of SC. We must examine the contents of interaction and dispositions that create social forces that attract or repel individuals (Hsu and Hung 2013). At the level of a single link, the nature of social flow leads to accumulation of SC. Social flows can be either benevolent and positive or negative and without benevolence. Whereas positive flow leads to net positive gain in SC, negative flow leads to loss of SC. Apart from social flow, dyadic ties may harbor trust or promote distrust. Trust supports SC, whereas distrust erodes SC. If the topic of interaction between the pair is centered on the main problem for the NO, that link positively contributes to SC. In sum, flow, trust, and topic are linked attributes that are proportional determinants for SC. Definition 1 Social capital in a link is measured from acProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence