Mining email to leverage knowledge networks in organizations

There is nothing new about the notion that in today‟s knowledge driven economy, knowledge is the key strategic asset for competitive advantage in an organization. Also, we have learned that knowledge is residing in the organization‟s informal network. Hence, to leverage business performance from a knowledge management perspective, focus should be on the informal network. A means to analyze and develop the informal network is by applying Social Network Analysis (SNA). By capturing network data in an organization, bottlenecks in knowledge processes can be identified and managed. But where network data can easily be captured by means of a survey in small organizations, in larger organizations this process is too complex and time-intensive. Mining e-mail data is more and more regarded as a suitable alternative as it automates the data capturing process and enables longitudinal research possibilities. An increasing amount of tools for mining e-mail data into social networks is available, but the question remains to what extent these tools are also capable of conducting knowledge network analysis: the analysis of networks from a knowledge perspective. It is argued that in order to perform knowledge network analysis, a tool is required that is capable of analyzing both the header data and the body data of e-mail messages. In this paper two e-mail mining tools are elaborated. One focuses on the analysis of e-mail header data and the other focuses on the analysis of e-mail body data. Both tools are embedded in their theoretical background and compared to other e-mail mining tools that address e-mail header data or e-mail body data. The aim of this paper is two-fold. The paper primarily aims at providing a detailed discussion of both tools. Continuing, from the in-depth review, the integration of both tools is proposed, concluding towards a single new tool that is capable of analyzing both e-mail header and body data. It is argued how this new tool nurtures the application of knowledge network analysis

The evaluation of social network analysis application's in the UK construction industry

The Social Network Analysis (SNA) has been adopted in the UK construction management research and there is a trend to apply it in large scale. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in the UK construction industry. From the review, the research proposed some improvement in the application of SNA in the construction industry

Participation In Agri-Food Safety Collaborative Network: An Example From Songkhla Province, Thailand

We conducted this case study in Songkhla Province in Thailand, with the aim of exploring the participation in a collaborative network for food safety. This study was conducted using a qualitative approach, with data collected from 15 representatives of various group leaders within the network. Participatory observation was used to cross-validate the data obtained, and content analysis to analyze the collected data. The study found that the goals of the agri-food safety collaborative network are self-reliance, resource conservation, food security, and health. The main purpose of the network is to develop a system for food-safety management, consisting of three connected systems: a fair and self-sufficient production system, a fair and sustainable marketing system, and an appreciative consumption. There are four supporting mechanisms for encouraging participation in the network: network management, coordination, mutual learning, and communication. We found that participation is a social learning process. The three systems of agri-food safety management focus on participation to encourage intra- and inter-group mutual learning of the network. The social capital existing in the area, especially, the civil society network and the knowledge therein, are key factors for building a collaborative network as a tool for the participation of the public and private sectors in the broader term of food safety

How can participatory social network analysis contribute to community-led natural resources management?: a case study from Bua Province, Fiji Islands

Thesis (M.S.) University of Alaska Fairbanks, 2015Adaptive co-management of natural resources requires a variety of stakeholders across different scales and sectors to communicate and collaborate effectively. Social network theory recognizes that stakeholders interact with each other through networks and that various network characteristics affect the way in which they function. Social relationships can be visualized through network mapping and their patterns systematically analyzed in a process known as social network analysis (SNA). Participatory SNA allows members of the network to be involved in the mapping or analysis process. Participants can then apply their knowledge of these relationships to build, improve, or better utilize their connections to increase desired outcomes. These actions are referred to as network interventions or network weaving. In Bua Province in the Fiji Islands, the Wildlife Conservation Society and other partners are facilitating "ridge to reef" ecosystem-based management planning and are striving to build local capacity for natural resources governance and conservation. This study seeks to determine how participatory SNA might be used as a tool for enhancing community-led natural resources management. First it was necessary to develop methods for conducting participatory SNA research with rural Fijian communities. Network data was then gathered from eight Districts and fifty villages. Social network maps were presented back to community stakeholders for their interpretation and to elicit their ideas for improving their resource governance networks. SNA was used to characterize and map patterns of information exchange and collaboration among stakeholders involved in natural resource management in Bua. Even without complete network data, several patterns emerged. These included: 1) Traditional decision-making networks that were more cohesive than information exchange networks, reflecting the importance of social hierarchies for decision making within rural Fijian communities and the need for resource governance to link into these structures. 2) All the District-level networks had a number of fragmented groups and more ties within than between communities. This highlights the challenge of getting communities to effectively collaborate at the District-level due to issues like distance between villages, conflicts, barriers to communication (e.g. no phone/internet), and clan-based (mataqali) land-ownership system. These issues suggest the need for innovative actions to help bridge these gaps and present an opportunity for network weaving. 3) Actor position analyses (indegree and outdegree) provided a list of opinion leaders and people who are good at reaching out to others. These individuals may be good candidates to receive network weaver trainings. These measures also highlighted individuals and groups that communities would like to work with in the future and who facilitators can help to connect. Overall, these results indicate that SNA can be a valuable tool for better understanding relationships between actors involved in collaborative natural resource management, but its use in rural settings can be limited by the challenges of collecting data in remote villages. The participatory process of evaluating networks with participants was beneficial since it helped communities recognize and discuss the strengths and weaknesses of their resource governance networks. This resulted in a list of recommended capacity-building activities (such as alternative livelihoods projects and special trainings for traditional leaders) based on their self-identified needs. However, the real potential benefits of this process will not be realized until the study results are applied, until network weaving and capacity building actually take place, and the process is evaluated to determine if any positive outcomes resulted for communities or conservation. This will require considerable commitment on the part of a network coordinator(s) to impart network concepts, facilitate network weaving activities, and in due course empower a transformation from the status quo to self-organizing, action-oriented conservation networks

Improving coordination in software development through social and technical network analysis

Today’s dynamic and distributed development environment brings significant challenges\ud for software project management. In distributed project settings, “management by walking\ud around” is no longer an option, and project managers may miss out on key project insights.\ud At the same time, the high coordination requirements caused by the dynamic distributed\ud environment can cause many coordination difficulties and can even lead to coordination\ud breakdowns. In response to some of these problems, researchers have developed detailed\ud patterns for describing the preferred relationships between the team communication structure\ud (the social network) and the technical software architecture. We call such patterns\ud Socio-Technical Patterns. As they capture a wide variety of knowledge and experience\ud Socio, Technical and Socio-Technical Patterns (or Socio/Technical Patterns in short) are\ud potentially very useful for the project manager in planning and monitoring complex development\ud projects. However, these patterns are hard to implement and monitor in practice.\ud The reason behind this is that it is difficult to find coordination problems in order to apply\ud the solutions provided by the Socio/Technical Patterns, as purely manual techniques are\ud labour intensive. Especially within dynamic and iterative distributed environments, the use\ud of Socio/Technical Patterns is challenging. But, even in small companies, employing between\ud 20 and 50 developers (ref Chapter 5 and 6), the social network and the relation to the\ud software tasks can get quite complicated for the software manager to track. As part of the\ud TESNA (TEchnical Social Network Analysis) project, we have developed a method and a\ud tool that a project manager can use in order to identify specific coordination problems that\ud we call Socio/Technical Structure Clashes (STSCs). We have evaluated the TESNA\ud method and tool in two commercial case studies (Chapters 5 and 6) and multiple case studies\ud in the Open Source development environment (Chapter 7)

Ontology modeling for generation of clinical pathways

Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP) as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM) is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM) is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the behavior will occur. To overcome this weakness, a semiotic approach to generation of clinical pathway is introduced. The CP generated from SAM together with norms will enrich the knowledge representation of the domain through ontology modeling, which allows the recognition of human responsibilities and obligations and more importantly, the ultimate power of decision making in exceptional circumstances

Current trends and future directions in knowledge management in construction research using social network analysis

The growing interest in Knowledge Management (KM) has led to increased attention to Social Network Analysis (SNA) as a tool to map the relationships in networks. SNA can be used to evaluate knowledge flows between project teams, contributing to collaborative working and improved performance. Similarly, it has the potential to be used for construction projects and organisations. This paper aims at identifying current trends and future research directions related to using SNA for KM in construction. A systematic review and thematic analysis were used to critically review the existing studies and identify potential research areas in construction specifically related to research approaches and explore the possibilities for extension of SNA in KM. The findings revealed that there are knowledge gaps in research approaches with case study-based research involving external stakeholders, collaborations, development of communication protocols, which are priority areas identified for future research. SNA in KM related to construction could be extended to develop models that capture both formal and informal relationships as well as the KM process in pre-construction, construction, and post-construction stages to improve the performance of projects. Similarly, SNA can be integrated with methodological concepts, such as Analytic Hierarchy Process (AHP), knowledge broker, and so forth, to improve KM processes in construction. This study identifies potential research areas that provide the basis for stakeholders and academia to resolve current issues in the use of SNA for KM in construction

Understanding Interactions between Design Team Members of Construction Projects Using Social Network Analysis

[EN] Social network analysis (SNA) has not been used to study design project teams in which the full interactions have become more complex (formal and informal) because the team members are from different companies and there is no colocation. This work proposes a method to understand the interactions in the design teams of construction projects using SNA metrics and the sociograms generated within temporary organizations. This study includes three stages: (1) a literature review of the dimensions of interactions within work teams and the application of SNA to the architecture, engineering, and construction (AEC) industry; (2) a proposal of an interaction network method for construction project design teams; and (3) an analysis of a pilot project. Interaction networks were defined in two categories: general interactions and commitment management. For each network, metric indicators were defined for the analysis. The pilot project showed high levels of consistency among team responses. The proposed method allows an analysis of the entire work team and of each individual team member. The method also makes it possible to analyze the work team from a global perspective by carrying out a joint analysis of two or more networks.The authors would like to acknowledge the help and support provided by GEPUC and GEPRO SpA., which provided access to data collection for this study. In addition, the authors acknowledge financial support from FONDECYT (1181648) and the Pontificia Universidad Catolica de Chile. Rodrigo Herrera acknowledges financial support for Ph.D. studies from VRI of PUC and CONICYT-PCHA/National Doctorate/2018-21180884.Herrera, RF.; Mourgues, C.; Alarcón, LF.; Pellicer, E. (2020). Understanding Interactions between Design Team Members of Construction Projects Using Social Network Analysis. Journal of Construction Engineering and Management. 146(6):1-13. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001841S1131466Alarcón D. M. I. M. Alarcón and L. F. 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Social network analysis as a tool for marine spatial planning: Impacts of decommissioning on connectivity in the North Sea

Connectivity of marine populations and ecosystems is crucial to maintaining and enhancing their structure, distribution, persistence, resilience and productivity. Artificial hard substrate, such as that associated with oil and gas platforms, provides settlement opportunities for species adapted to hard substrates in areas of soft sediment. The contribution of artificial hard substrate and the consequences of its removal (e.g. through decommissioning) to marine connectivity is not clear, yet such information is vital to inform marine spatial planning and future policy decisions on the use and protection of marine resources. This study demonstrates the application of a social network analysis approach to quantify and describe the ecological connectivity, informed by particle tracking model outputs, of hard substrate marine communities in the North Sea. Through comparison of networks with and without artificial hard substrate, and based on hypothetical decommissioning scenarios, this study provides insight into the contribution of artificial hard substrate, and the consequence of decommissioning, to the structure and function of marine community connectivity. This study highlights that artificial hard substrate, despite providing only a small proportion of the total area of hard substrate, increases the geographic extent and connectivity of the hard substrate network, bridging gaps, thereby providing ‘stepping stones’ between otherwise disconnected areas of natural hard substrate. Compared to the baseline scenario, a decommissioning scenario with full removal of oil and gas platforms results in a nearly 60% reduction in connectivity. Such reduction in connectivity may have negative implications for species’ distribution, gene flow and resilience following disturbance or exploitation of marine hard substrate communities. Synthesis and applications. Social network analysis can provide valuable insight into connectivity between marine communities and enable the evaluation of impacts associated with changes to the marine environment. Providing standardized, transparent and robust outputs, such a tool is useful to facilitate understanding across different disciplines, including marine science, marine spatial planning and marine policy. Social network analysis therefore has great potential to address current knowledge gaps with respect to marine connectivity and crucially facilitate assessment of the impacts of changes in offshore substrate as part of the marine spatial planning process, thereby informing policy and marine management decisions

Command and Control in the Information Age: A Case Study of a Representative Air Power Command and Control Node

As operations command structures change, it is important to be able to explore and understand their fundamental nature; researchers should unearth the gestalt nature of the operational node. The organizational structure and the infrastructure can significantly affect overall command and control (C2) performance. Thus, it is necessary to develop understanding of effectiveness of the technical network and the people using the system as a whole. The purpose of this research is to conduct an analysis of a representative Air Power Operational C2 node, create and use a repeatable method, and present the results as a case study to elicit fundamental understanding. I posit that there is a recognizable (and discoverable) relationship between the social (human) network and technical supporting network. Examining the system under change can result in an understanding of this relationship. In this work, I enhanced an existing simulation tool to investigate the effects of organizational structure on task effectiveness. The primary research question examined is how a representative AOC system changes varying noise and system fragmentation when operating in two different organizational constructs. Network-Enabled Capability (as the term is used in NATO), Network Centric Operations, or Edge Organizations, is a core C2 transformation predicated upon a set of network-centric tenets. These tenets form the intellectual foundation for ongoing transformations. The secondary research question is to determine if these tenets are unbound, and what elucidation results if they are not. This research produces four significant contributions to Operational Command and Control and Engineering Management disciplines. First, I combined social networking theory and information theory into a single lens for evaluation. By using this new concept, I will be able to accomplish a quantitative evaluation by something other than mission treads, field exercise, historical evaluation, or actual combat. Second, I used both information theory and social networking concepts in a non-traditional setting. Third, I hope this research will start the process required to gain the knowledge to achieve some sort of future C2 structure. Fourth, this research suggests directions for future research to enhance understanding of core Operational Command and Control concepts