Knowledge Management

An Interdisciplinary Approach for Business Decisions

Shaofeng Liu

• Éditeur: Kogan Page
• Année: 2020
• ISBN:
  9780749496876
• Édition: 1
• Sujet: Business
• Sous-sujet: Knowledge Capital

As knowledge economies become increasingly important around the world, it is essential that organizations are able to transform their knowledge into a competitive advantage. This textbook offers an interdisciplinary approach to knowledge management written specifically for postgraduate students in business and management schools. Knowledge Management presents classic and advanced concepts, models and frameworks using a clear logical structure, which covers building knowledge competence, the knowledge lifecycle, and integration of knowledge management with business decision making. An overall framework illustrates links between chapters and ensures readers can gain a body of actionable knowledge rather than learning isolated, uncontextualized topics.Based on cutting-edge research findings and covering the most advanced IT and IS technologies, this book emphasises the need for knowledge management to span boundaries across organizations, supply chains and partnerships, rather than being limited to individual learning and sharing within businesses. Knowledge Management is international in scope and includes real world case studies and role play scenarios to show how theories are applied in practice, and "think back" and "critique discussion" questions to encourage reflective learning and critical thinking. This indispensable text provides a dynamic picture of the evolution of knowledge management and demonstrates its full potential to enable better business decisions. Accompanying online resources include PowerPoint slides for lecturers and exercise questions for students.

Part one

Knowledge management – key concepts and models

This first part of the book lays the foundation for knowledge management and business decisions. Part One consists of three chapters:

• Chapter 1 includes definitions of knowledge and knowledge management, why knowledge and knowledge management are important to individuals, organizations and the economy, some well-known knowledge management approaches, and the multidisciplinary perspectives of knowledge management.
• Chapter 2 classifies knowledge into categories. Great attention has been paid to the concepts of tacit and explicit knowledge. This chapter also discusses the four knowledge processes for conversion between tacit and explicit knowledge, known as the SECI model (Socialization, Externalization, Combination and Internalization). Knowledge ‘Ba’ theory, which provides knowledge spaces for knowledge conversion, is discussed.
• In Chapter 3, knowledge management processes are analysed first, including the knowledge building stage, knowledge holding stage, knowledge mobilization stage and knowledge utilization stage. Following the discussion of the four main stages of the knowledge management process, knowledge lifecycle models are examined in detail.

01

Introduction to knowledge management

Learning outcomes

After completing this chapter, the reader will be able to:

• define knowledge and knowledge management in different contexts;
• understand the key benefits to individuals, organizations and the economy – the value created by knowledge management;
• describe different knowledge management approaches;
• distinguish between different perspectives in the knowledge management literature.

1.1 What is knowledge and knowledge management?

As a relatively young subject, knowledge management (KM) has gained enormous popularity in the post-industrial era. In order to facilitate the discussion of KM, it is essential to have some understanding of what knowledge is. Hence, this section starts with the definition of knowledge.

Knowledge definition

Drucker (1988) identified that knowledge was the most important resource for individuals, businesses, governments, nations and society at large, but knowledge is complex by nature. While knowledge is increasingly being viewed as a valuable commodity or intellectual asset, that is, embedded in businesses, delivered in the form of products (especially high-technology products) and embodied in highly mobile employees, there are some paradoxical characteristics of knowledge that are radically different from other value products. This is the very reason why KM can stand as a distinctive subject in its own right, rather than a sub-topic of product development such as design, manufacture or management. These knowledge characteristics include (Dalkir, 2017):

• Using knowledge does not consume it.
• Sharing and transferring knowledge does not result in losing it.
• Knowledge is abundant, but the ability to use knowledge is scarce.
• Much of an organization’s valuable knowledge walks out of the door at the end of the day.

Despite the popularity KM has gained, there has been so far no consensus on what knowledge is. Many definitions have been provided over the years, and a few of them are given here:

Knowledge can be defined as ‘justified true belief related to human actions’. (Nonaka and Takeuchi, 1995)

Knowledge consists of truths and beliefs, perspectives and concepts, judgements and expectations, methodologies and know-how. It is possessed by humans, agents or other active entities, and is used to receive information and to recognize and identify; analyse, interpret, and evaluate; synthesize and decide; plan, implement, monitor and adapt – that is, to act more or less intelligently. In other words, knowledge is used to determine what a specific situation means and how to handle it. (Wiig, 1999: 179–202)

Knowledge is ‘a fluid mixture of framed experience, values, contextual information and expert’s insight that provides a framework for evaluating and incorporating new experience and information. It originates and is applied in the mind of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms’. (Davenport and Prusak, 1998: 5)

Knowledge is a high value form of information that is ready to apply to decisions and actions. (Davenport and Prusak, 2000)

Knowledge could be considered as ‘actionable information’. (Jashapara, 2011: 18).

Knowledge in an area is defined as ‘justified beliefs about relationships among concepts relevant to that particular area’. (Becerra-Fernandez and Sabherwal, 2015: 18)

Knowledge refers to ‘the understanding of people about relationships among phenomena. It is embodied in routines for the performance of activities, in organizational structures and processes and in embedded beliefs and behaviour. Knowledge implies an ability to relate inputs to outputs, to observe regularities in information, to codify, explain and ultimately to predict’. (North and Kumta, 2018: 36)

Even though these definitions of knowledge look quite different in expression, there are some commonalities that we can get from the interpretation and implications below the surface:

• Knowledge is closely related to human ‘action’, ‘judgements’, ‘activities’ and ‘prediction’, which positively suggest decision making.
• People are a key element of knowledge because it is related to ‘human’ action, the understanding of ‘people’, and possessed by ‘humans, agents or other entities’. This view makes it comfortable for people who are decision makers to make good use of knowledge for making better decisions.
• Knowledge is contextualized because it has ‘a specific situation’ and is in ‘a particular area’ and ‘organizational structures’. This fits well with the point that decisions are made within a specific culture, situation and environment.
• Knowledge is related to, but more valuable than, information in supporting decision making, because knowledge is ‘justified’, ‘truths’, ‘actionable’ and ‘ability’. This point has support from the well-known ‘data–information–knowledge ladder’ (North and Kumta, 2018: 35), but this book extends the ladder from knowledge to decisions by closing the gap through KM and business decision making, as illustrated in Figure 1.1.

Figure 1.1 Extending the ‘data–information–knowledge ladder’ to decisions

Figure 1.1 details

The figure shows the ‘data–information–knowledge’ ladder on the left. The steps in sequence are:

• Symbols;
• Data (With ‘syntax’ added);
• Information (With ‘meaning’ added);
• Knowledge (With ‘context’ added).

The above steps from data to knowledge indicate the ‘Scope of data–information–knowledge ladder.’

On the right, the ladd...