Kathrin Kirchner

Personal Knowledge Management: The role of Web 2.0 tools for managing knowledge at individual and organisational levels

Purpose – The purpose of this paper is to discuss new approaches for managing personal knowledge in the Web 2.0 era. The paper questions whether Web 2.0 technologies (social software) are a real panacea for the challenges associated with the management of knowledge. Can Web 2.0 reconcile the conflicting interests of managing organisational knowledge with personal objectives? Does Web 2.0 enable a more effective way of sharing and managing knowledge at the personal level?Design/methodology/approach – Theoretically deductive with illustrative examples.Findings – Web 2.0 plays a multifaceted role for communicating, collaborating, sharing and managing knowledge. Web 2.0 enables a new model of personal knowledge management (PKM) that includes formal and informal communication, collaboration and social networking tools. This new PKM model facilitates interaction, collaboration and knowledge exchanges on the web and in organisations.Practical implications – Based on these findings, professionals and scholars wil...

Social Media in Organizations: Leveraging Personal and Collective Knowledge Processes

By using social media, many companies try to exploit new forms of interaction, collaboration, and knowledge sharing through leveraging the social, collaborative dimension of social software. The traditional collective knowledge management model based on a top-down approach is now opening up new avenues for a bottom-up approach incorporating a more personal knowledge management dimension, which could be synergized into collective knowledge using the social-collaborative dimension of social media. This article addresses the following questions: (1) How can social media support the management of personal and collective knowledge using a synergetic approach? (2) Do the personal and collective dimensions compete with each other, or can they reinforce each other in a more effective manner using social media? Our findings indicate that social media supports both the personal and collective dimensions of knowledge, while integrating a social collaborative dimension. The article introduces a framework that classifies social software into four categories according to the level of interaction and control. With certain tools, individuals are more in control. With other tools, the group is in control, resulting in a higher level of interaction and a diversity of knowledge and mindsets brought together. However, deploying and adopting these new tools in an organizational context is still a challenging task for management, owing to both organizational and individual factors.

What factors influence knowledge sharing in organizations? A social dilemma perspective of social media communication

Purpose Enterprise social media platforms provide new ways of sharing knowledge and communicating within organizations to benefit from the social capital and valuable knowledge that employees have. Drawing on social dilemma and self-determination theory, the purpose of this paper is to understand what factors drive employees’ participation and what factors hamper their participation in enterprise social media. Design/methodology/approach Based on a literature review, a unified research model is derived integrating demographic, individual, organizational and technological factors that influence the motivation of employees to share knowledge. The model is tested using statistical methods on a sample of 114 respondents in Denmark. Qualitative data are used to elaborate and explain quantitative findings. Findings The findings pinpoint towards the general drivers and barriers to knowledge sharing within organizations. The significant drivers to knowledge sharing are: enjoy helping others, monetary rewards, management support, management encourages and motivates knowledge sharing behavior and knowledge sharing is recognized. The significant identified barriers are: change of behavior, lack of trust and lack of time. Practical implications The proposed knowledge sharing framework helps to understand what factors impact engagement on social media. Furthermore, the article suggests different types of interventions to overcome the social dilemma of knowledge sharing. Originality/value The study contributes to an understanding of factors leading to the success or failure of enterprise social media drawing on self-determination and social dilemma theory.

How wikis can be used to manage knowledge in SMEs A case study

Companies are exploring new ways of knowledge sharing, collaboration and communication with employees, partners and customers, using social media tools like wikis, blogs or social networks. This article briefly reviews the literature concerning the introduction of social media into organizations and reports a small case study in an IT consulting company following the introduction of a wiki-based solution into its daily practices. The usage of the wiki and its impact on knowledge work are explored. The authors then compare the findings from the literature review with those from the case study. The comparison shows that introducing knowledge processes can start ‘bottom-up’ through an initiative to better manage personal knowledge. Furthermore, the articulation of personal and collective knowledge is possible synergistically and in a more transparent manner.

Reusable components for partitioning clustering algorithms

Clustering algorithms are well-established and widely used for solving data-mining tasks. Every clustering algorithm is composed of several solutions for specific sub-problems in the clustering process. These solutions are linked together in a clustering algorithm, and they define the process and the structure of the algorithm. Frequently, many of these solutions occur in more than one clustering algorithm. Mostly, new clustering algorithms include frequently occurring solutions to typical sub-problems from clustering, as well as from other machine-learning algorithms. The problem is that these solutions are usually integrated in their algorithms, and that original algorithms are not designed to share solutions to sub-problems outside the original algorithm easily. We propose a way of designing cluster algorithms and to improve existing ones, based on reusable components. Reusable components are well-documented, frequently occurring solutions to specific sub-problems in a specific area. Thus we identify reusable components, first, as solutions to characteristic sub-problems in partitioning cluster algorithms, and, further, identify a generic structure for the design of partitioning cluster algorithms. We analyze some partitioning algorithms (K-means, X-means, MPCK-means, and Kohonen SOM), and identify reusable components in them. We give examples of how new cluster algorithms can be designed based on them.

Embedding conformance checking in a process intelligence system in hospital environments

Process intelligence is an effective means to analyze and improve business processes in companies with high degree of automation. Hospitals are also facing high pressure to be profitable with ever decreasing available funds in a stressed healthcare sector, which calls for methods to enable process management and intelligent methods in their daily work. However, traditional process intelligence systems work with logs of execution data that is generated by workflow engines controlling the execution of a process. But the nature of the treatment processes requires the doctors to work with a high freedom of action, rendering workflow engines unusable in this context. In this paper, we introduce a novel method to conformance checking that computes fitness of individual activities in the setting of sparse process execution information, i.e., not all activities of a patients treatment are logged. We embed this method into a process intelligence approach for hospitals without workflow engines, enabling process monitoring and analysis.

An architecture for component-based design of representative-based clustering algorithms

We propose an architecture for the design of representative-based clustering algorithms based on reusable components. These components were derived from K-means-like algorithms and their extensions. With the suggested clustering design architecture, it is possible to reconstruct popular algorithms, but also to build new algorithms by exchanging components from original algorithms and their improvements. In this way, the design of a myriad of representative-based clustering algorithms and their fair comparison and evaluation are possible. In addition to the architecture, we show the usefulness of the proposed approach by providing experimental evaluation.

Social Media Collaboration in the Classroom: A Study of Group Collaboration

This article aims to investigate how students use new technology in collaborative group work and tries to measure what factors impact students’ satisfaction with overall group collaboration. In particular, this study aims to investigate the following research questions: What are the factors (including challenges) that influence the students’ overall satisfaction with collaboration? Does the usage of e-collaboration tools and social media usage influence collaboration satisfaction? The findings of the study are summarized in a model that point towards the main factors influencing student overall group work satisfaction.

Finding best algorithmic components for clustering microarray data

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.

Modeling and Monitoring Variability in Hospital Treatments: A Scenario Using CMMN

Healthcare faces the challenge to deliver high treatment quality and patient satisfaction while being cost efficient which is tackled by introducing clinical pathways to standardize the treatment processes. At the Jena University Hospital, the clinical pathway for living donor liver transplantation was modeled using Business Process Model and Notation. A survey based on that model investigates on the transferability of this pathway to other hospitals and lists the requirements for a general model including the need for flexibility caused by differences in treatments in various hospitals. In this paper, we show an approach to tackle the requirements for such a flexible process by using the Case Management Model and Notation standard. Further, we show how case monitoring and analysis can be established by using an approach combining event processing and case management. The holistic approach is exemplified by using a scenario of the evaluation of living liver donors.