Abstract
AbstractKnowledge Management (KM) is considered by many organizations a key aspect in sustaining competi-tive advantage. Designing appropriate KM process and enabling technology face considerable risks, as they must be shaped to respond to specific needs of the organizational environment. Thus, many systems are abandoned or fall into disuse because of inadequate understanding of the organizational context. This motivates current research, which tends to propose agent organizations as a useful paradigm for KM systems engineering. Following these approaches, organizations are analyzed as collective systems, composed of several agents, each of them autonomously producing and managing their own local data according to their own logic, needs, and interpreta-tive schema, i.e. their goals and beliefs. These agents interact and coordinate for goal achievement defining a coherent local knowledge system. This paper presents a novel methodology for analyzing the requirements of a KM system based on an iterative workflow where a pivotal role is played by agent-oriented modeling. Within this approach, the needs for KM systems are traced back to the organization stakeholders goals. A case study is used to illustrate the methodology. The relationship of this work with current studies in agent organizations and organizational knowledge management is also discussed. Differently from other works, this methodology aims at offering a practical guideline to the analyst, pointing out the appropriate abstractions to be used in the different phases of the analysis.
Highlights
Agents have frequently been proposed as appropriate entities to enable the analysis and design of complex systems, made up of several components that often behave in a distributed fashion and interact with each other in order to achieve a common objective [15,26,27]
With the main target of filling in this gap, this paper presents a novel methodology for analyzing the requirements of a Knowledge Management (KM) system, adopting an agent-oriented approach
If a technological solution is needed, agents enable legacy systems to be considered in the analysis, allowing the new solution to be based on approaches of integration of old and new components. This may lead to more satisfaction to end users, who are already familiar with the interface and methods applied in the systems in use. These aspects are compliant with the Distributed Knowledge Management approach [1] which prescribes that more attention should be given the knowledge holders and the natural processes they already use to share knowledge within organizations, which leads to a bottom-up strategy when proposing a KM solution
Summary
Agents have frequently been proposed as appropriate entities to enable the analysis and design of complex systems, made up of several components that often behave in a distributed fashion and interact with each other in order to achieve a common objective (i.e. the system’s overall functionality) [15,26,27]. Based on the analysis of the current social structures embedded in the organization may lead to more appropriate system proposals to enable such structure to evolve in terms of efficiency and performance This paradigm seems even more natural when carried out to the Knowledge Management (KM) domain, defining social behavior and processes underlying the organizational settings [6,12,14,18]. We claim that more focus should be given to the initial phases of system development, aiming at grasping the requirements of the system to be, both in terms of the individual perspective of the organizational members and the overall objectives of the organization This is especially important in the KM context, which focuses on the effective use of human intellectual capital, since much of human knowledge is tacit and intangible [16]. The paper is organized as follows: section 2 discusses how agent organizations, starting from the analysis of agent’s cognitive mental structures, could support modeling of organizational KM settings; section 3 presents this work’s proposed approach for systems requirements analysis and the applied notation; section 4 presents a fictitious KM scenario used here to illustrate our novel methodology; section 5 focuses on a case study for our methodology, using the scenario of section 4; section 6 discusses related work; and, section 7 presents conclusion and future directions of this work
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