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Qualitative analysis of a social knowledge management initiative in an inovation institute
Abstract
During the information era, the knowledge became a competitive differential. The knowledge management, despite of not been a new subject, has increased its importance. In this scenario, the use of corporative social networks supporting organizations knowledge management initiatives turns out to be a promising approach. This article presents the results of a qualitative analysis about the knowledge management initiative based in social networks made in a Brazilian innovation institute. The analysis points out some problems and suggests some improvements in organizational knowledge management processes definition.
... Estes acontecimentos trouxeram novos desafios ao ciclo pesquisado, pois a popularização e a manutenção da iniciativa deveriam continuar, mesmo com a dissolução da equipe. O trabalho [28] apresenta o terceiro ciclo em maiores detalhes. ...
The usage of social networks environments inside organizations is being suggested as a good way to deploy knowledge management initiatives. Because this kind of environment provides a natural and informal way to people engage on collaborative activities, this kind of initiative can be a good alternative approach to achieve better results in knowledge management initiatives. In this context, it was conducted an action research to investigate how a Web based social network could be used as the main tool in a knowledge management initiative being held in an innovation institute. During this action research, the Web based social network was proposed and developed to meet the knowledge management needs of the organization. In the five years span in which the action research was carried out, a set of metrics were proposed, monitored and analyzed, originating some improvement proposals for both, the initiative and the social network environment. Through the deployment of these proposals, it was possible to monitor the impacts of the proposals on the organization. At the end, this action research showed some reflections and conclusions on the whole initiative.
The paper targets to devise a genuine Knowledge Management (KM) performance measurement model in a stochastic setting based on Data Envelopment Analysis (DEA), Monte Carlo simulation and Genetic Algorithm (GA). The proposed model evaluates KM using a set of proxy measures correlated with the major KM processes. Data Collection Budget Allocation (DCBA) that maximizes the model accuracy is determined using GA. Additional data are generated and analyzed using a Monte-Carlo-enhanced DEA model to obtain the overall KM efficiency and KM processes’ efficiency scores. An application of the model has been carried out to evaluate KM performance in higher educational institutions. It is found that with GA, the accuracy of the model has been greatly improved. Lastly, comparing with a conventional deterministic DEA model, the results from the proposed model would be more useful for managers to determine future strategies to improve their KM.
This case study describes the effects of using a Web Based Social Network (WBSN) approach to Knowledge Management in a Brazilian
software development organization. During this work it was verified how the previous approach to Knowledge Management, a wiki
based approach, did not work to this organization and how users voluntarily migrated from this environment to the WBSN. In
addition to the WBSN environment, a Knowledge Management process has been proposed, with some metrics related to its phases.
These metrics have been monitored since October 2006 and analyzed in order to verify the efficiency of this approach. In order
to give a better understanding of the concepts related to Social Networks and Knowledge Management, it is presented a brief
introduction to each one of them, including an evaluation of existing Knowledge Management approaches.
In this paper, we report the result of a research project investigating social aspects of knowledge sharing and development. Prior research in a consulting firm revealed that respondents recognized five kinds of informational benefits when consulting others: solutions, meta-knowledge, problem reformulation, validation and legitimation. We employed these dimensions in a systematic network analysis of a different sample of people (human resource managers in a large conglomerate), using each of the five benefits as kinds of social relations. Two general research questions guided the analysis. First, how are these relations related to each other (multiplexity)? Do individuals obtain all of the benefits from the same individuals, or do they create balanced portfolios of complementary contacts that provide different benefits? Second, what properties and shapes do the networks induced by these relations form (structure)? What is the basis for who is tied to whom on each relation? The fundamental result emerging from both research questions is that the five relations seem to form a unidimensional scale such that a contact who provides any given benefit is also very likely to provide all the benefits that are lower on the scale. Position on this scale seems to index underlying dimensions of social solidarity rather than individual attributes such as status. Consequently, relations at the end of the scale (e.g. legitimation) were more homophilous and proved to be strongly diagnostic of subgroup boundaries, a fact which could be quite useful in consulting or other applied contexts. This research contributes to the literature on knowledge management by revealing diverse ways that consulting others facilitates knowledge creation and utilization. The research also contributes to social network analysis by examining meanings and relationships among social relations, an area that is understudied. We found that the five benefits, treated as social relations, formed an entailment structure consistent with a Guttman scale. We also found that relations lower in the scale flowed smoothly across historical organizational boundaries whereas relations higher in the scale did not.
Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise. The authors investigate the following areas concerning social networks: how to exploit our unprecedented wealth of data and how we can mine social networks for purposes such as marketing campaigns; social networks as a particular form of influence, i.e.., the way that people agree on terminology and this phenomenon's implications for the way we build ontologies and the Semantic Web; social networks as something we can discover from data; the use of social network information to offer a wealth of new applications such as better recommendations for restaurants, trustworthy email senders, or (maybe) blind dates; investigation of the richness and difficulty of harvesting FOAF (friend-of-a-friend) information; and by looking at how information processing is bound to social context, the resulting ways that network topology's definition determines its outcomes.
Introduction The issue of how to support the re-use of knowledge --- under rubrics such as organizational memory, knowledge management and expertise management --- has received increasing attention over the last decade. In this chapter we take a strongly social approach to the issue, arguing that knowledge (and expertise) is created, used, and disseminated in ways that are inextricably entwined with the social milieu, and therefore that systems which attempt to support these processes must take social factors into account. Our approach to managing knowledge or expertise is to do it on-line, via multiuser networked environments that support group communication and collaboration. That is, we are interested in designing on-line environments within which users can engage socially with one another, and, in the process, discover, develop, evolve, * To appear in To Appear in Beyond Knowledge Management: Sharing Expertise. (eds. Ackerman, Mark, Volkma
In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model
of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics
emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an
analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.
For years, companies have strived to manage knowledge more effectively, the primary motivation being improved business performance. However, not all knowledge management (KM) styles may be equally effective. This paper analyses how various KM styles affect performance. Fifty-four firms were selected as the sample base for an empirical test. The test results are presented here. It was found that KM methods can be categorized into four styles: dynamic, system-, human-oriented, and passive. The emphasis of the dynamic style is on both (i) knowledge reusability through information technologies and (ii) knowledge sharing through informal discussions among employees. This dynamic style results in higher performance. Human- and system-oriented styles do not show any difference in terms of corporate performance; the passive style is less effective. This outcome is in line with previous observations that both tacit and explicit knowledge are important in capitalizing on corporate knowledge.
Vast numbers of free/open source software (F/OSS) development projects use hosting sites such as Java.net and Source-Forge.net. These sites provide each project with a variety of software repositories (e.g. repositories for source code sharing, bug tracking, discussions, etc.) as a media for communication and collaboration. They tend to focus on supporting rich collaboration among members in each project. However, a majority of hosted projects are relatively small projects consisting of few developers and often need more resources for solving problems. In order to support cross-project knowledge collaboration in F/OSS development, we have been developing tools to collect data of projects and developers at SourceForge, and to visualize the relationship among them using the techniques of collaborative filtering and social networks. The tools help a developer identify "who should I ask?" and "what can I ask?" and so on. In this paper, we report a case study of applying the tools to F/OSS projects data collected from SourceForge and how effective the tools can be used for helping cross-project knowledge collaboration.
Over several years, there have been intensive discussions about the importance of knowledge management (KM) within the business community. Effectively implementing a sound KM strategy and becoming a knowledge-based company is seen as a mandatory condition of success for organizations as they enter the era of the knowledge economy. However, standardized metrics are needed to quantify knowledge and to fully convince management and stakeholders as to the value of KM initiatives. Development of KM metrics has begun in recent years and these metrics are being applied by some organizations, but more research is needed to better define these measures and to make them universal. The purpose of this research is to survey and report the current measures of knowledge assets or intellectual capital, as well as the methods that are popularly being followed by organizations to measure the performance of KM strategies. The research findings should: assist organizations in identifying the measures that are appropriate and suitable for them, for improving the quality of metrics they use for measuring KM effectiveness; and assist researchers in identifying future research needs toward the standardization of KM measurement metrics.
One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected profit from sales to her). We propose to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a collaborative filtering database. Marketing that exploits the network value of customers -- also known as viral marketing -- can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.
- S Staab
Staab, S. et al., 2005. Social Networks Applied. IEEE Intelligent
Systems, 20(1), 80-93.