A Multi-Agent Knowledge Management System for Reactive and Proactive Knowledge Supply

This chapter presents an agent-based architecture for integrating organizational knowledge repositories and business processes orchestrated by a workflow management system. This architecture proactively provides relevant knowledge to workflow tasks considering their context, and stores the information generated by its execution for future requirements. It describes components of the architecture, models a multi-agent system that enables the integration, presents a strategy to annotate and retrieval knowledge of non-structured information sources, and defines a new workflow pattern to be used in knowledge intensive tasks in order to make possible the knowledge provision. This architecture allows workers to count, in a proactive way, with all necessary information for the task executions without suspending their activities to retrieve information scattered in the organization. It reduces the wasted time in manual knowledge searches included in mostly knowledge management approaches.

How Are Professional Skills Acquired?

As firms lose skilled labour force through mass retirement, turnover and economic fluctuations, a pressing concern is how to ensure that the skills of the present-day masters are conveyed to the future professionals. This chapter examines the process of how the skills leading to professional performance are acquired in the context of on-the-job learning and identifies the key factors in this process. Based on an empirical examination of the on-the-job training periods of 20 Finnish vocational college students a model is built, suggesting that there are three different levels of professional performance on a junior’s path towards professionalism. The results suggest that individual activity, critical observation, conscious doing and creativity are crucial in the learning process. The chapter contributes to the emerging discussion on knowledge sharing across generations in organisations.

Dynamics in Knowledge

Since the idea of “artificial intelligence with knowledge” had been introduced, so many thoughts, theories, and ideas in various fields of engineering, science, geology, social study, economics, and management methods have been proposed. Those things have been started as an extension of modern engineering control theories and practices. Firstly, expert system by using IF-Then rules came up to at a production spot in manufacturing, and then agent system method by using intelligent software programs for design, planning, scheduling, production, and management in manufacturing. And then after, the idea of “Knowledge” burst into the artificial intelligence field as a real aid for getting any purpose to be accomplished by having augmented the past key knowledge in terms of management (controlling). However, those augmented knowledge methods used to have usages only in a limited small area. In addition to this, lots of works have to be done before making the systems work for a target problem solving. And what is worse, lots of parts of systems have to be customized for a new application. This chapter introduces a new direction and a method in “Knowledge” by inaugurating the brand new idea of “Dynamics in Knowledge,” which will behave more flexibly and intelligently in real usages.

Alternative Call Center Operational Indicators to Customer Satisfaction

The purpose of this chapter is to purport a set of alternative call center operational performance indicators, in order to enhance the relationship between call center performance and customer satisfaction. In order to do that, a methodological approach based on surveys, as well as stepwise multiple linear regressions, is developed from 6,616 cases collected during three months from the telecommunication industry. The general conclusion is that a set of alternative call center indicators covering three dimensions, namely the call center ability to resolve a problem, the call center responsiveness, and the prior customer satisfaction with the call center, together with traditional indicators, present a statistically significant relationship with customer satisfaction. The insights from this study can help managers to improve the customer satisfaction with call center, as well as to better sub-contract outsourcing call center operators.

Framework of Knowledge and Intelligence Base

This chapter introduces the concept of the “Intelligence Base,” developed in a study on the information requirement of the management of an (military) organization. The purpose of the study was to conceive, for each level of an organization, an appropriate Decision Support System (DSS) and/or Knowledge and Information System. All systems would eventually have been integrated in an overall Enterprise Architecture (EA). By discussing the OODA-cycle of John Boyd and the Naturalistic Decision Making (NDM) the authors show that the concept of Intelligence Base can be a DSS for very demanding environments. Related topics are knowledge, culture, and real options (business example). The proposed framework is based on Service-Oriented Architecture (SOA) and Cloud Computing, which will determine the configuration of the Information Technology (IT) systems.

A Predictive Modeling of Retail Satisfaction

Data mining allows managers to make more knowledgeable decisions by predicting future trends and behaviors. One of the most widely used areas of data mining for the retail industry is in marketing. There are also at least seven methods of analysis or statistical techniques that are commonly used in data mining. It is obvious that the approach to the data mining is the key determinant of the statistical technique to be used. Predictive modeling uses variety of techniques such as linear regression, logistic regression, and their extensions can be used to identify patterns, which can be used to predict the future. This research specifically focuses on the application of multiple regression technique a data mining tool in Indian retail industries to predict the retail satisfaction using store attributes as independent variables.

Adverse Events and Medical Errors in Greece

For years, experts have recognized that medical errors exist and compromise healthcare quality. Much has been written worldwide about medical errors and improvements in their reporting and handling, with the proposals ranging from the implementation of nationwide mandatory reporting with public release of performance data to voluntary reporting and quality-assurance efforts that protect the confidentiality of error-related data. In the present chapter, the author first points out the lack of standardized nomenclature and a universal taxonomy-classification for adverse events and medical errors, which complicates the development of a response to these issues. The chapter also reviews a number of methods of and adverse events’ and medical errors’ knowledge management, each of which has evolved over time and been adapted to different contexts. Finally, the author assesses each of these methods, unveiling their particular strengths and advantages, and also weaknesses and limitations.

Achieving Competitive Advantage through Innovation

Innovation in the service/retail sector has not been fully examined in the non-Western literature. This preliminary work presents a study that was conducted in Shanghai, China. Three sets of the literature are consulted: Porter’s Generic Strategies Model, Hunt’s Resource Advantage Theory (R-A), and previous studies in the service and retail sectors. Findings developed from six selected successful Chinese supermarket companies have identified three types of innovation adopted by Chinese retailers: Technology based, non-technology based, and resource based innovation. The study takes a qualitative approach by using the methods of documentation survey and in-depth interviews with a panel of ten supermarket experts. Some managerial implications are explicated, and the limitations of the study and directions for future studies are discussed.

Knowledge Management in Educational Games

Use of educational games during teaching process does not represent a new topic. However, the question “How to address the knowledge in educational games?” is still open. The purpose of this chapter is to propose a model that will attempt to establish the balance between knowledge integration into game on one side, and its reusability on other. The model driven approach presented in this chapter relies on the use of Learning Objects (LO) as constructing pieces of knowledge resources, which are specialized for educational game design purpose. Presented models contribute to methodology of educational games development in a way that they embrace principles of learning and knowledge management early in design process. The authors demonstrate applicability of their models in design case study, where they developed educational game editor where the educator can easily define new educational game utilizing existing knowledge, assessment, and multimedia from repository.

Enhancing Information Retrieval Capabilities of Knowledge Management Systems

User support services face complex problems in the efficient and satisfactory delivery of services to users. Knowledge management (KM) principles can be effectively implemented within this organizational context to support efficient and effective problem solving to improve service delivery to the users. A KM system with good information retrieval capabilities is critical in empowering front line employees to utilize organizational knowledge repositories for better service delivery. The purpose of this chapter is to present different aspects of the major information retrieval techniques that can be used in a user services environment and to propose new models to enhance retrieval capabilities of KM systems. The authors discuss the basic elements that make up an information retrieval system including metadata, controlled, and uncontrolled vocabularies. The authors also propose three experimental search interfaces for enhancing information retrieval capabilities of a KM system. The first uses a thesaurus for enhanced retrieval features through better query formulation and browsing of search results; the second uses the tag cloud concept to present thesaurus terms; and the third combines the structure of a controlled vocabulary with the flexibility of a folksonomy and tag cloud, thus incorporating the beneficial aspects of both uncontrolled and controlled vocabularies to support retrieval within a heterogeneous corporate environment.