How different connectivity patterns of individuals within an organization can speed up organizational learning

Knowledge sharing within a cooperative organization is an important issue since the power of its outcome has been the principal source of competitive advantage over the competitors in the market. However, without a proper collective knowledge management, its utilization as a strategic weapon or competitive advantage becomes difficult and inefficient. From an organizational perspective, the most important aspect of knowledge management is to transfer knowledge. In this regards, organizations must adopt structures that allow them to create and transfer more knowledge. Organizational communication structure affects the nature of human interactions and information flow which in its own turn can lead to a competitive advantage in the knowledge economy. However, in addition to that, social relationships between individuals in an organization can also be utilized to produce positive returns. In this article we emphasize the role of individual structural importance within an organizational informal communication structure as a mechanism for knowledge flow and speeding up organizational learning. Our experimental results indicate the fact that structural position of individuals within their informal communication networks can help the network members to have a better access to ongoing information exchange processes in the organization. The results of our analyses also show that organizational learning through an informal communication network of people in the form of scale-free connectivity pattern is faster comparing to the small-world connectivity style.     

The deep structure of organizational online networking – an actor‐oriented case study

While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large‐scale implementation of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi‐dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers and information retrievers as two mutually interdependent actor roles as an explanation for uneven levels of user contributions to the SNS. Based on our analysis, we elicit abstract order principles, such as topical discourses, and identify transactive memory theory as a potent explanation of the evolving interaction structures. We finally discuss how the deep structure framework can contribute to future research on organizational networks.     

IT‐mediated social interactions and knowledge sharing: Role of competence‐based trust and background heterogeneity

In the knowledge‐based economy, organizational success is dependent on how effectively organizational employees share information. Many studies have investigated how different types of communication activities and communications media influence knowledge sharing. We contribute to this literature by examining increasingly prevalent yet understudied IT‐mediated social interactions and their effects on knowledge sharing among employees in comparison to face‐to‐face social connections. By integrating the literature on knowledge sharing, social networks, and information systems, we theorize the ability of IT‐mediated social interaction to (1) afford interactions between individuals with heterogeneous backgrounds and (2) facilitate frequent IT‐mediated social interactions that are high in competence‐based trust—both supporting effective sharing of knowledge. Through a social network analysis of the employees in a high‐tech organization, this study finds that IT‐mediated frequent social interactions are the most effective in promoting knowledge sharing.     

Enabling agile adoption practices through network organizations

As distributed organizations increasingly rely on technological innovations to enhance organizational efficiency and competitiveness, interest in agile practices that enable adoption of information technology (IT) based innovations has grown. This study examines the influence of a network organization environment on the ability to develop agile adoption practices. An exploratory case study design was used to investigate the interactions between network structure, social information processing, organizational similarity (homophily), and absorptive capacity during the adoption of a large-scale IT system in two network organization environments within New York State. The data suggest that network organization characteristics and communication processes that reinforced social influence and supported knowledge transfer positively influenced adoption agility. We propose a model of agile adoption practices and discuss implications for the development of theory about network organization characteristics and capabilities to adopt IT-based innovations.     

Networking Beyond Organizational Boundaries: The Case of Project Organizations

Project organizations operate in environments where innovation depends significantly on the ability to integrate different but interrelated knowledge bases. These knowledge bases include individuals who are located outside organizational boundaries and have no formal relationship with the organization, but are connected socially to project workers. Organizational researchers have generally recognized the importance of external social relations for knowledge search. However there is some debate on the question of whether social networks are more useful for innovation if they provide social cohesion through close interaction or access to diverse and novel sources of knowledge through more distant relationships. This study explores the configuration of project workers’ external social networks and their effects on innovative behaviour, using data on the network ties of workers in 17 project organizations in the new‐media industry. The findings are more consistent with the social embeddedness view of close social relations providing an important source of continuity in markets where intermittent projects are common. Project workers embedded in cohesive work‐related social structures outside the organization tend to be more innovative in their project work than workers lacking such networks.     

Supporting community-based learning: case study of a geographical community organization designing its website

Community computing supports human - computer interaction among neighbours in geographical or place-based community organizations. Using a case study of such an organization, we investigate the process of designing their website. Our long-term participatory design approach, integrating developmental informal learning, allowed us to understand how this community organization adopts, evaluates, and sustains website technology. Based on our case study analysis, we present three design heuristics for developing community-based technology: align and afford new possibilities for participation, dynamically manage organizational knowledge and learning, and enhance social capital within community organizations and with the broader community.     

Relationship Identification Across Heterogeneous Online Social Networks

In the era of the social web, many people manage their social relationships through various online social networking services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if combining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real‐world social networks, Sina Weibo and Jiepang, demonstrate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check‐in patterns are among the most discriminative features for relationship identification.     

Managers’ and Employees’ Differing Responses to Security Approaches

ABSTRACT

Modern organizations face significant information security threats, to which they respond with various managerial techniques. It is widely believed that “one size does not fit all” for achieving employee information security policy compliance; nevertheless, it is yet to be determined which techniques work best to different organizational employees. We further this research stream by finding that different levels of users might be effectively motivated by different types of coercive and empowering techniques that are suitable to their level and position in the organizational chart. Our results suggest that participation in the ISP decision-making process might prove to be a more effective approach to motivate lower-level employees toward compliance and that enhancing the meaningfulness of policy compliance could be the preferred method among higher levels of management. Members within each level of the organization can be effectively influenced to comply with ISPs when such strategies are customized for their level.     

Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior

We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks.     

科学家、组织与技术及其关系的社会网络分析

社会网络分析能够对社会网络中行为者之间的关系进行量化分析,以可视化的图形界面展示节点之间的深层关系及群体结构。本文从维基百科中采集计算机网络领域内前沿网络技术、突出贡献科学家和学术信息权威组织机构的相应信息,构建网络技术与科学家、组织机构与科学家等多种类型的关系网络,从中心性、凝聚性及核心-边缘结构等方面进行社会网络分析,得到热点技术、核心研究团队等计算机网络发展趋势上的若干特征。     

The effects of network characteristics on performance of innovation clusters

Industry clusters provide not only economic benefits but also technological innovation through networking within a cluster. In this study, we analyze network-specific structural and behavioral characteristics of innovation clusters with the intention of delving into differences in learning performance in clusters. Based on three representative networks of real world, scale-free, broad-scale, and single-scale networks, the learning performance of entire organizations in a cluster is examined by the simulation method. We find out that the network structure of clusters is important for the learning performance of clusters. Among the three networks, the scale-free network having the most hub organizations shows the best learning performance. In addition, the appropriate level of openness that maintains long-lasting diversity leads to the highest organizational learning performance. This study confirms the roles of innovation clusters and implies how each organization as a member of a cluster should run their organization.     

Strategic choices of inter-organizational information systems: A network perspective

As cooperation in a networked manner increases via various inter-organizational information systems (IOISs), it is important to choose appropriate IOISs for different types of organizations in the network environment. In this study, we analyzed customer-supplier relationships among organizations in five industries using social network analysis (SNA) methods and empirical data, aiming to help organizations strategically choose appropriate IOISs. Three types of customer-supplier networks were identified based on the network centralization comparison rate: customer-centric, supplier-centric and balanced networks. Based on the empirical findings in our analysis, we then propose strategies about how to choose appropriate IOISs for the firms in these networks and discuss the pros and cons of the choices. To the best of our knowledge, this is the first empirical research that applied SNA methods to study customer-supplier networks in the context of inter-organizational information systems.     

Hyperlinks as Institutionalized Connective Public Goods for Collective Action Online

Nongovernmental organization (NGO) hyperlink networks are institutionalized connective public goods. They influence which actors and what aspects of social issues are made visible to the public in search engine results. To understand how contextual forces and institutional pressures influence who hyperlinks to whom online, this research examines a hyperlink network of 410 NGOs with various social missions operating across China. It suggests that institutional factors external to the network impact the structure of NGO hyperlink networks. In particular, institutional convener and legal registration status induce homophily hyperlinking effects among organizations, controlling for network interdependencies and other organizational attributes. Implications for hyperlink networks, institutional homophily, NGO collective action, and the development of civil society in China are drawn from the results.     

Semantic Technology for Capturing Communication Inside an Organization

Using semantic technologies in various domains, researchers have developed domain-specific ontologies to capture knowledge and enable reasoning. Organizations can support such knowledge management by capturing knowledge of their own people and their communication records, including email exchanges. The proposed approach analyzes an internal social network and uses the resulting information to produce an informal organizational structure. The authors evaluated their approach using a mid-size organization's actual data and compared the informal structure they obtained with the formal organizational structure. As the results show, the approach proved useful for modeling social structures based on real-world communication records.     

新浪微博隐式组织发现

社交网络中往往同时存在多种类型的账号,如正常个体用户、水军、僵尸粉、蓝V组织等。我们把其行为呈现为组织特性的个体账号,定义为隐式组织。隐式组织通常背后有相应的组织团队负责账号的运营,因此其行为模式呈现为组织的行为模式,有别于个体账号。隐式组织的有效发现对于社交网络中舆情传播趋势分析、广告推荐等都有重要的意义。该文以新浪微博数据为例,在数据采集系统基础上,共人工标注了583个账号,提取了22个特征,使用朴素贝叶斯和决策树算法,实现了对隐式组织的有效识别,其准确率达86.4%,并分析得出了特征的重要程度排序。实验证明了社交网络中存在隐式组织,其行为特征是可以识别的。     

Organizational information security as a complex adaptive system: insights from three agent-based models

The management of information security can be conceptualized as a complex adaptive system because the actions of both insiders and outsiders co-evolve with the organizational environment, thereby leading to the emergence of overall security of informational assets within an organization. Thus, the interactions among individuals and their environments at the micro-level form the overall security posture at the macro-level. Additionally, in this complex environment, security threats evolve constantly, leaving organizations little choice but to evolve alongside those threats or risk losing everything. In order to protect organizational information systems and associated informational assets, managers are forced to adapt to security threats by training employees and by keeping systems and security procedures updated. This research explains how organizational information security can perhaps best be managed as a complex adaptive system (CAS) and models the complexity of IS security risks and organizational responses using agent-based modeling (ABM). We present agent-based models that illustrate simple probabilistic phishing problems as well as models that simulate the organizational security outcomes of complex theoretical security approaches based on general deterrence theory (GDT) and protection motivation theory (PMT).     

Individual learning goal orientations in self‐managed team‐based organizations: A study on individual and contextual variables

Previous research has shown the importance of individual learning goal orientation for both job and task performance and consequently organizational performance. Despite its importance, knowledge on the antecedents of learning goal orientation remains scarce, especially in the context of self‐managing team‐based organizations. In fact, most of the research on goal orientation antecedents has been focused on individual characteristics, belief, and ability, while the contextual factors that might influence them remain unspecified. We build on and further extend earlier studies by jointly exploring the role of individual and contextual factors affecting individual learning orientation. In particular, this study combines individual informal social network, self‐efficacy, performance feedbacks, and team identification into a model that explains individuals' learning goal orientation within self‐managing team‐based organizations. The model was empirically tested on a sample of 104 individuals belonging to an R&D organization relying on self‐managing teams. Results show that performance feedback has a negative direct effect, while team identification has a positive direct effect on individual learning goal orientation. In addition, we found that individual self‐efficacy is a mediator of the relationships between performance feedback and brokerage in the advice network and individual learning goal orientation. Finally, we did not find a relationship between centrality in the friendship network and individual learning goal orientation.     

Caught between the users and the firm: How does identity conflict affect employees' innovative behavior

Absorbing external knowledge is crucial for innovation within the organization. One way of tapping external knowledge sources is to rely on employees who reach out across the firm's boundary to external stakeholders and address knowledge sets located beyond the organizational boundary. However, such employees are likely to identify with the stakeholders they reach out to which exposes them to potentially conflicting demands—with positive or negative effects for their employing organization. We investigate whether and how their dual identification with the organization and with users, and the potential identity conflicts this engenders, affects their job satisfaction and innovativeness. We study a sample of 243 employees in two industries, revealing that perceived conflict between organizational identification and user identification detracts from job satisfaction if and only if employees are strongly identified with both targets. We find also that identity conflict is indirectly and negatively related to innovative work behavior through job satisfaction. Our paper contributes to the literature on the benefits and risks of employee ties to external stakeholders. We contribute also to research on embedded users by elucidating under what conditions they are most valuable to their employing organizations.     

THE EFFECT OF NETWORK STRUCTURE ON DYNAMIC TEAM FORMATION IN MULTI-AGENT SYSTEMS

Previous studies of team formation in multi-agent systems have typically assumed that the agent social network underlying the agent organization is either not explicitly described or the social network is assumed to take on some regular structure such as a fully connected network or a hierarchy. However, recent studies have shown that real-world networks have a rich and purposeful structure, with common properties being observed in many different types of networks. As multi-agent systems continue to grow in size and complexity, the network structure of such systems will become increasing important for designing efficient, effective agent communities.
We present a simple agent-based computational model of team formation, and analyze the theoretical performance of team formation in two simple classes of networks (ring and star topologies). We then give empirical results for team formation in more complex networks under a variety of conditions. From these experiments, we conclude that a key factor in effective team formation is the underlying agent interaction topology that determines the direct interconnections among agents. Specifically, we identify the property of diversity support as a key factor in the effectiveness of network structures for team formation. Scale-free networks, which were developed as a way to model real-world networks, exhibit short average path lengths and hub-like structures. We show that these properties, in turn, result in higher diversity support; as a result, scale-free networks yield higher organizational efficiency than the other classes of networks we have studied.