Capability Maturity

The dependence of any organization on knowledge management is clearly understood. Actually, we should distinguish between knowledge management (KM) and knowledge engineering (KE): KM is to define and support organizational structure, allocate personnel to tasks, and monitor knowledge engineering activities; KE is concerned with technical matters, such as tools for knowledge acquisition, knowledge representation, and data mining. We shall use the designation KMKE for knowledge management and knowledge engineering collectively. KM is a very young area—the three articles termed “classic works” in Morey, Maybury, and Thuraisingham (2000) date from 1990, 1995, and 1996, respectively. We could regard 1991 as the start of institutionalized KM. This is when the Skandia AFS insurance company appointed a director of intellectual capital. KE has a longer history—expert systems have been in place for many years. Because of its recent origin, KMKE is characterized by rapid change. To deal with the change, we need to come to a good understanding of the nature of KMKE.

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

Interactive Knowledge Acquisition Tools: A Tutoring Perspective Yolanda Gil (gil@isi.edu) and Jihie Kim (jihie@isi.edu) Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292, USA Abstract This paper argues that interactive knowledge ac- quisition tools would benefit from a tighter and more thorough incorporation of tutoring and learn- ing principles. Current systems learn from users in a very passive and disengaged manner, and could be designed to incorporate the proactive capabili- ties that one expects of a good student. This pa- per points out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly. We discuss how a more thorough and explicit representation of these principles would help improve enormously how computers learn from users. Introduction Computers have long been considered an invaluable tool for education. Intelligent tutoring systems and other kinds of educational software show how people can acquire knowledge about diverse topics by inter- acting with a computer. Given our reliance in com- puters for the future of education, we need to realize that an important component of the human educa- tion revolution is the computer education revolution: anyone should be able to teach computers on topics that are of value, so that anyone can learn about those topics from computers. So an important ques- tion is: how will computers acquire knowledge? In most cases knowledge is entered by hand by software or knowledge engineers, as is often done in intelligent tutoring systems (Forbus & Feltovich, 2001). This limits severely the utility of the tools, as it would be more desirable that the people with expertise in the domain at hand would be the knowledge providers. Knowledge can also be extracted from text (Cowie & Lehnert, 1996), although given the error rates of state of the art systems and the kinds of knowledge they acquire (mostly instance-level information) it will take many years for these techniques to be of practical use to build an accurate body of knowl- edge about a topic domain. Another possibility is to use interactive knowledge acquisition tools that help users enter knowledge. In recent years these systems have shown that end users with no background in computer science or knowledge representation were able to enter sizeable amounts of knowledge (Kim & Gil, 1999; Eriksson et al, 1995; Clark et al, 2001). Although interactive knowledge acquisition tools enable end users to enter knowledge, users remain largely responsible for the acquisition process, both the teaching side and the learning side. These tools are quite passive in terms of formulating or pursuing learning goals, keeping track of the flow of a lesson, and generally assess how much they are learning and how useful that knowledge is. At the same time, users are not necessarily skilled teachers by nature, so being in a position to teach a computer is al- ready a challenge for them. Interactive acquisition tools need to be more effective and helpful to users, perhaps by incorporating some of the skills that are expected of good students. And, as good students do, they should also be able to cope with an inexpe- rienced teacher (which their users are likely to be) and still learn from the experience by bringing to bear knowledge about how a good teacher typically goes about a lesson. An interactive acquisition tool could then be viewed as a tool to support augmented cognition, since it would supplement the user’s lim- itations as a teacher and knowledge engineer. The contributions of this work are twofold. First, we point out how existing knowledge acquisition tools use techniques that are related to widely used tutoring and learning principles. Second, we identify areas that the acquisition tools developed to date have neglected, and suggest promising areas of re- search based on our findings. This would result in a new generation of acquisition tools that are not only better students but also more helpful to the teacher (the user). The paper begins with a short introduction and background on interactive knowledge acquisition tools. We then discuss several tutoring and learn- ing principles that we have drawn from the educa- tional literature and that seem useful to support the interactive acquisition process. Next, we show how some existing acquisition tools use techniques that are related to these principles in some aspects of their functionality. We finalize with a discussion of promising directions that we see in designing acqui- sition tools that incorporate tutoring and learning principles more thoroughly.

This paper presents a knowledge management system for crop disease. The aim of KMSCD is to provide a knowledge management tool for efficient knowledge acquisition, storage, knowledge engineering, processing and proper maintenance of knowledge that can be ultimately used by the diagnostic expert system. The development of the KMSCD simplifies the complete process of knowledge management by providing user-friendly interface to the domain expert for entering and storing the domain specific knowledge to solve the disease identification and control problem particularly for oilseeds crops. The system presently applies to the knowledge management of 25 prevalent diseases of three major oilseeds crops of India viz. soybean, groundnut and rapeseed mustard. The adopted development methodology and the experience acquired in the knowledge engineering and development of knowledge management system for crop disease are discussed in this paper.

Using natural language sources in model-based knowledge acquisition

In this study, the authors found it appropriate to demonstrate the role, place and importance of engineering knowledge in the management practice of managing with a manufacturing plant, considering these issues from the perspective of the cost leadership strategy. When elaborating on engineering knowledge, special attention here should be paid to the development and analysis of the quality of products, based on a study of customer expectations, and to express these expectations by defining the relevant parameters of quality and technology. Taking up the discussion on the issues of organizational learning, the authors formulated the hypothesis corresponding to the adopted goals of this development: The cost leadershipstrategy, which is the result of the organization learning process is an important factor affecting the growth of the implemented product. Therefore, managing with managerial- -engineering knowledge is a key factor in engineering management strategy, including the cost management strategies.

Knowledge is a critical enabling factor for healthy agri-food innovation systems (AIS). AIS and related knowledge management (KM) frameworks face significant implementation challenges. We review applications of KM to AIS, the current state of the art and shortcomings and present a new KM framework, Agricultural Knowledge Management for Innovation (AKM4I). Previous agricultural KM frameworks do not integrate innovation pragmatically, use linear, reductionist, top-down pathways to innovation, and do not explicitly incorporate issues of power, politics, ownership, and trust when combining scientific and local knowledge across multiple stakeholders. The AKM4I framework addresses systemic interactions favouring innovation outcomes by formalising flows and management of information and knowledge between diverse sets of stakeholders; and explicitly considering previously unresolved practical and relational barriers aiming to facilitate more equitable, rapidly evolving, and actionable knowledge generation and management for innovation and transformational change. An agricultural case study serves as an example of the implementation of AKM4I.

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools.

Automated knowledge acquisition (AKA) tools hold promise of preventing the process of acquiring knowledge from becoming the bottleneck in the development of knowledge base systems (KBSs). Most hospitals keep detailed records of patients containing descriptions of symptoms, diagnosis, prescribed medicine, and observed changes over time. Cumulatively, these records contain a wealth knowledge about medical diagnosis and treatment. The knowledge acquisition process would be significantly simplified if an automated system could look at these records, and extract valuable pieces of knowledge. Doctors would only need to verify the output of these knowledge acquisition tools rather than sit through hours of interview with knowledge engineers. This paper presents a new methodology for knowledge acquisition from databases. The p roposed methodology coincide with RITIO algorithm 13) in the rule induction without drawing the decision tree and in the eliminating less effective attributes. It builds the decision tree when needed. The new methodology differs from the ID3' likes algorithms (15) and RITIO since it Gods the global optimal solutions via back tracking. It is tested on standard example and applied on a real world database.

A fundamental shift in the preferred approach to building applied artificial intelligence (AI) systems has taken place since the late 1960s. Previous work focused on the construction of general-purpose intelligent systems; the emphasis was on powerful inference methods that could function efficiently even when the available domain-specific knowledge was relatively meager. Today the emphasis is on the role of specific and detailed knowledge, rather than on reasoning methods. The first successful application of this method, which goes by the name of knowledge-based or expert-system research, was the DENDRAL program at Stanford, a long-term collaboration between chemists and computer scientists for automating the determination of molecular structure from empirical formulas and mass spectral data. The key idea is that knowledge is power, for experts, be they human or machine, are often those who know more facts and heuristics about a domain than lesser problem solvers. The task of building an expert system, therefore, is predominantly one of “teaching” a system enough of these facts and heuristics to enable it to perform competently in a particular problem-solving context. Such a collection of facts and heuristics is commonly called a knowledge base. Knowledge-based systems are still dependent on inference methods that perform reasoning on the knowledge base, but experience has shown that simple inference methods like generate and test, backward-chaining, and forward-chaining are very effective in a wide variety of problem domains when they are coupled with powerful knowledge bases. If this methodology remains preeminent, then the task of constructing knowledge bases becomes the rate-limiting factor in expert-system development. Indeed, a major portion of the applied AI research in the last decade has been directed at developing techniques and tools for knowledge representation. We are now in the third generation of such efforts. The first generation was marked by the development of enhanced AI languages like Interlisp and PROLOG. The second generation saw the development of knowledge representation tools at AI research institutions; Stanford, for instance, produced EMYCIN, The Unit System, and MRS. The third generation is now producing fully supported commercial tools like KEE and S.1. Each generation has seen a substantial decrease in the amount of time needed to build significant expert systems. Ten years ago prototype systems commonly took on the order of two years to show proof of concept; today such systems are routinely built in a few months. Three basic methodologies—frames, rules, and logic—have emerged to support the complex task of storing human knowledge in an expert system. Each of the articles in this Special Section describes and illustrates one of these methodologies. “The Role of Frame-Based Representation in Reasoning,” by Richard Fikes and Tom Kehler, describes an object-centered view of knowledge representation, whereby all knowldge is partitioned into discrete structures (frames) having individual properties (slots). Frames can be used to represent broad concepts, classes of objects, or individual instances or components of objects. They are joined together in an inheritance hierarchy that provides for the transmission of common properties among the frames without multiple specification of those properties. The authors use the KEE knowledge representation and manipulation tool to illustrate the characteristics of frame-based representation for a variety of domain examples. They also show how frame-based systems can be used to incorporate a range of inference methods common to both logic and rule-based systems. "Rule-Based Systems,” by Frederick Hayes-Roth, chronicles the history and describes the implementation of production rules as a framework for knowledge representation. In essence, production rules use IF conditions THEN conclusions and IF conditions THEN actions structures to construct a knowledge base. The autor catalogs a wide range of applications for which this methodology has proved natural and (at least partially) successful for replicating intelligent behavior. The article also surveys some already-available computational tools for facilitating the construction of rule-based knowledge bases and discusses the inference methods (particularly backward- and forward-chaining) that are provided as part of these tools. The article concludes with a consideration of the future improvement and expansion of such tools. The third article, “Logic Programming, ” by Michael Genesereth and Matthew Ginsberg, provides a tutorial introduction to the formal method of programming by description in the predicate calculus. Unlike traditional programming, which emphasizes how computations are to be performed, logic programming focuses on the what of objects and their behavior. The article illustrates the ease with which incremental additions can be made to a logic-oriented knowledge base, as well as the automatic facilities for inference (through theorem proving) and explanation that result from such formal descriptions. A practical example of diagnosis of digital device malfunctions is used to show how significantand complex problems can be represented in the formalism. A note to the reader who may infer that the AI community is being split into competing camps by these three methodologies: Although each provides advantages in certain specific domains (logic where the domain can be readily axiomatized and where complete causal models are available, rules where most of the knowledge can be conveniently expressed as experiential heuristics, and frames where complex structural descriptions are necessary to adequately describe the domain), the current view is one of synthesis rather than exclusivity. Both logic and rule-based systems commonly incorporate frame-like structures to facilitate the representation of large amounts of factual information, and frame-based systems like KEE allow both production rules and predicate calculus statements to be stored within and activated from frames to do inference. The next generation of knowledge representation tools may even help users to select appropriate methodologies for each particular class of knowledge, and then automatically integrate the various methodologies so selected into a consistent framework for knowledge.

PurposeMany authors have recognised multiple research gaps at empirical and theoretical levels regarding the interplay between the management of intellectual capital (IC) and the development of innovation capabilities from a strategic approach to technological opportunities, especially in knowledge-intensive business services (KIBS). This paper endeavours to provide answers to some of these research questions by relying on empirical observations in the context of an emergent economy.Design/methodology/approachAfter reviewing the literature through the lenses of the IC and knowledge-based view, the paper operationalises IC as a formative construct and explores its relationships with Technological Orientation, Innovativeness and Innovative Performance. This investigation employs structural equation modelling to analyse data collected through a questionnaire from a sample of KIBS members in a cross-sectional study.FindingsThe research confirms the positive influence of human capital on a company’s Technological Orientation, which in turn impacts Innovative Performance in KIBS. Structural and relational capital positively affect Innovativeness and the latter directly impact performance. All other IC components were found to determine the dependent variables through a mediated effect.Research limitations/implicationsThis research has several limitations. Firstly, it has to be stressed that the significance of the statistical findings is dependent on the sample size, and the results’ explanatory power and effect size cannot be extrapolated to other demographics in different regions or sectors. Secondly, it should be observed that the results of the empirical research can be affected by the respondents’ subjectivity in assessing the items and their perception of reality when providing the answers. Thirdly, this is a cross-sectional study, and results concerning, e.g. the Innovative Performance may be subject to change over a longer period of time, though items in this construct have inquired about results obtained over time. Finally, given the purpose of the research, the outcome may be limited to the research model design. Future research could attempt to replicate the results by investigating other demographics and other regions and by employing a longitudinal study.Practical implicationsThe study highlights the value added by adequate management of all IC elements, as they affect the output of organisational business strategies and processes in an integrated manner. An organisational technological strategy that is based on up-to-date technologies, the embeddedness of technology in the organisational processes and the staff’s continuous technical/digital upskilling are conducive to competitive advantage and business success in KIBS. High competencies and skills that are continuously renewed through training as well as the acquisition of digital competencies, play an influential role in generating innovative capabilities that lead to business performance. Flexible and efficient business processes, the company’s support for informal knowledge sharing between employees and its ability to exploit new knowledge to sustain growth are prerequisites for KIBS’ success. Promoting an organisational culture of trust and collaboration, making the staff feel valued and satisfied and effectively using digital technologies for communication are all significant factors in supporting the development of innovation capabilities.Social implicationsThis research proves that IC management, viewed as an integrated construct comprising human, structural and relational capital management, significantly affects Innovativeness through the development of technology-based innovative capabilities. Furthermore, in a context where KIBS (and other companies) nowadays compete in a digital environment, technology and technical knowledge are paramount for a company’s success. The study validates in an aggregated model the role played in innovation by the management of knowledge and technical knowledge embodied in the employees’ competencies and know-how, the management of tacit and explicit knowledge developed by the structural capital and the management of stakeholder knowledge accumulated by the relational capital.Originality/valueThis research takes a fresh and comprehensive perspective to investigate how IC management can impact Innovativeness and Innovative Performance in KIBS by focusing on the nexus with the organisations’ Technological Orientation. The paper makes an original contribution to research concerning IC, KIBS, technology and innovation based on empirical evidence and a new approach to assessing IC as an aggregate rather than by looking at individual components.

This paper analyzed the main features of product design and manufacturing in the knowledge management environment and service oriented management model based on collaborative. Based on the analysis of these two aspects, this paper puts forward the four key problems which the collaborative design activities in manufacturing supply chain will face in knowledge representation and knowledge converting and knowledge reuse and knowledge management. At last, a framework for collaborative product design(CPD) knowledge management model in manufacturing supply chain based on ontology is established under the application background of integrated platform for collaborative product design in manufacturing enterprise.

Interest in examining the role of leaders' personalities in teamwork and their influence on management style selection arises from the perception that existing research inadequately addresses these aspects, both theoretically and methodologically. Few efforts have been made to explore the influence of subjective factors, particularly leaders' personality characteristics, on management style choices in teamwork. This chapter, apart from its introduction and conclusion, comprises seven cohesive units within the theoretical framework. The practical aspect of the research involves statistical analysis using descriptive statistics (frequency and percentages) and analysis of variance to examine differences among respondents' assessments of team leaders' leadership styles' impact on team productivity based on demographic and status characteristics (such as gender, age, and level of professional education). Various statistical tests, including the F-test and Levene's test, were employed. Additionally, the study draws upon relevant literature and updated work practices to inform its findings, employing the aforementioned methodologies.Keywords: leadership; leadership qualities; intellectual capital; leadership style; creativity.JEL Classification: O34; O15. Cite this chapter:Šprajc, P., Podbregar, I., & Arsenijević, O. (2024). Leadership Dynamics and Knowledge Management in Organisation. Exploring the Impact of Management Styles on Intellectual Capital. In: Knowledge Management in Economy, Technology and Education, N. Perić and O. Arsenijević (Eds.). (pp. 85-110). Book Series Socio-Economics, Research, Innovation and Technologies. RITHA Publishing. https://doi.org/10.57017/2024.SERITHA.KM-ETE.ch4  Chapter’s history: Received 3rd of November, 2023; Revised 7th of February, 2024; Accepted for publication 28th of May, 2024; Published 30th of July, 2024. About the Author(s):Polona Šprajc is a full professor and serves as the Vice Dean for Education and Public Relations at the Faculty of Organisational Sciences, University of Maribor. Her extensive research interests encompass human resource management, crisis management, and risk management. P.S. has made significant contributions to these fields through her research, teaching, and leadership roles. She is actively involved in various academic and professional initiatives aimed at advancing knowledge and practices in organisational science. For more information on Polona Šprajc's academic work and contributions, please visit her profiles: Faculty of Organisational Sciences Profile or Researcher Profile on COBISSIztok Podbregar is a full professor and currently serves as the Dean at the Faculty of Organisational Sciences, University of Maribor. His research interests include organisational science and management, crisis management, and security management. Podbregar, I., has an illustrious career that extends beyond academia. He has served as the Chief of the General Staff of the Slovenian Army and as the Director of the Slovenian Intelligence and Security Agency. In addition to being a respected scientist, he has had notable roles as a politician, general, and military pilot. For more information, please visit his profile: Faculty of Organisational Sciences Profile.Olja Arsenijević is a full professor at the Institute of Serbian Culture Pristina – Leposavić. Her research interests span several critical areas, including leadership, change management, knowledge management, and intellectual capital. Arsenijević, O. is deeply engaged in exploring how leadership and effective management of knowledge and intellectual resources can drive organisational success and innovation. Her work is instrumental in understanding and implementing strategies that foster organisational change and the development of intellectual capital. For more information on Olja Arsenijević's academic work and contributions, please visit her profile: Institute of Serbian Culture Profile.

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.