Knowledge Management in the Innovation Process

Many of the unresolved problems in economic policy, management and research turn on questions of knowledge. This is not new, but it becomes more important as technologies facilitate the transmission and the use of knowledge and make its management a practical reality. This raises questions, which this book addresses. However, it is not a book about technology. It is about knowledge, its management, and the implications of knowledge management for innovation.

Knowledge management, as the most recent offspring of the study of organizational learning, seems to have developed as one of the hot fads in academia and business. Many publications have appeared during the last few years (Dierkes et al. Second Edition, 2001), ranging from a quite abstract treatment of the role of knowledge in organizational learning and transformation to efforts to motivate businesses to pay more attention to the knowledge base of the organization and the continuous development of its intellectual resources. A wide range of devices to improve and enhance the use of the knowledge base of an organization have been recommended. They encompass such diverse concepts as the `chief knowledge officer’ (CKO) or the `corporate university’, which are mostly a new label for what used to be called the human resource and management development function. The common understanding in most of the literature is that the knowledge base of an organization is more important than ever before to ensure its survival in the competitive global market (Nonaka & Reinmöller, 1998).

There is little in the extensive literature on knowledge management, or in the related new business practices, to indicate what is new. The many historical examples suggest that there have always been elaborate forms of knowledge management, whether in the context of the network of Cistercian abbeys that constituted an effective system for the dissemination of new agricultural techniques (Gimpel, 1995); the company craft guilds in the Middle Ages that took on the essential functions of embodying and transmitting practical knowledge (Epstein, 1998); or, finally, the large metallurgical and chemical companies in the early 20th century which ensured the links between research and development and learning through application by doing (Caron, 1997). Knowledge and learning have thus always been central to the economy and “learning organizations” have always existed. Why, then, talk about new developments and ruptures?²’

This chapter addresses one question: do business practices in the matter of knowledge management determine creativity of manufacturing firms? Creativity is measured with a question concerning the extent to which product and process innovations are developed primarily by the firm, primarily by other organizations, or jointly by the firm and other organizations. To deal with this question, we review the literature on innovation in order to see why creativity and knowledge came to be added to the traditional explanatory variables of innovation. The literature suggests distinguishing codified from uncodified business practices of knowledge management. This chapter distinguishes five forms of uncodified practices of knowledge management: business network, information network, research network, participation, and relational assets. As for the forms of codified practices of knowledge management, we distinguish four forms: acquisition of knowledge embodied in advanced technologies, creation of knowledge through R&D activities, capacity of knowledge management through the ratio of scientists and engineers in firms, and structured mechanisms for sourcing knowledge. Barriers concerning the lack of cooperation with providers of knowledge, the number of employees and sales are also used as explanatory variables of the creativity of firms. The data used for this chapter come from an innovation survey administered from April to June 2000 to 440

From existing information, what can we say about the flows of knowledge from the public sector to firms? How well is it managed and what are the impacts? Granted, the data sources are incomplete and often secondary but they do provide some insights into the importance and practice of KM.

A recent headline in a major German newspaper read `Many companies pay lip service only to knowledge management - have no knowledge about their own competencies and knowledge is not shared with staff’. The article’ referred to a research study that surveyed top managers of 112 of Germany’s largest 500 companies with respect to their companies’ practice of knowledge management^2.. The study found a huge gap between the perceived need for knowledge management and the companies’ actual practice. Thus for instance, 98 percent of the management believes that regular systematic environmental scans about future trends and developments are a strategic necessity, yet only 20 percent are conducting such comprehensive scans. Only 28 percent of the companies communicate to their own staff the results of their scans. Data from outside the company flows primarily to top management, strategic planning, and sales, but not to the research and development (R&D) and production units. Knowledge about the companies’ own competencies is underdeveloped as only 26 percent of firms are looking into the effects of personnel fluctuation and thus the effects of the drain of the firm’s knowledge base.

At the level of the enterprise, the knowledge capital of firms is presumed to be the dominant source of competitive advantage. When this issue was first recognized, attention was focused on quantifying the production of new knowledge obtained through the employment of new technology. At the beginning of the 1970’s, a first wave of data collection on research and experimental development (R&D) activity was introduced by the Organisation for Economic Co-operation and Development (OECD). A second wave during the 1990’s dealt with technological innovation. This latter initiative led to the development of a number of new indicators at both the input as well as at the output level. These indicators can be summarized as follows. On the input side, the innovation cost indicator is now available (see Eurostat, 1997). It includes more than the usual bulk of R&D expenditures and better fits the resource allocation for innovation. International comparisons appear to show that this measurement indicator is quite reliable since the share of innovation costs is quite homogenous among the European Community (E.C.) countries (Eurostat, 1997, 1998). However, though firms are aware of how many dollars they invest in the innovation process, it seems that they are not able to precisely map the different kinds of resources that are involved (see Evangelista et al., 1994 on CIS1;

In this chapter, I will discuss the word `practice’ in the context of knowledge and knowledge management partially. This will allow us to focus both on tacit and explicit knowledge. But what is a practice? To approach this question, I would like to discuss eight heuristics that carry with them assumptions that are fairly well accepted among knowledge practitioners regarding notions of `understanding’.

Since its establishment in 1916, the National Research Council of Canada (NRC) has played a number of roles for the Federal Government according to the priorities of the time. Research excellence has always been a primary focus. Until the 1980s, NRC’s research generally followed internally generated priorities and was seen as part of the `public good’. Research teams predominantly worked apart from industry although firms did come to NRC for expert advice and funding through the Industrial Research Assistance Program (IRAP).

What is the marginal utility of Heisenberg’s uncertainty principle or Durkheim’s anomie theory? What is the marginal utility of a test that can detect BSE in cows that are still alive, or younger than one year old? Very difficult questions indeed.

Innovation policy is centrally concerned with stimulating, guiding and monitoring knowledge-based activities within a political jurisdiction, typically a nation or a region. The goals of innovation policy are economic, although they are widely stated in broad welfare terms (e.g. the advancement of knowledge, sustainable development, or social benefits). Its instruments are programs and institutions, as well as ideas. However, as a policy area, not only is it deeply knowledge intensive but its subject itself is knowledge. Hence, given the wide variation of knowledge - from the explicit to the tacit - knowledge management in innovation policy is a domain that is fraught with complexity, ambiguity, uncertainty, judgment, creativity and spontaneity.

As Nico Stehr, in this volume, describes so well, knowledge, for the economist, has a strange characteristic: sharing or circulating it does not reduce the portion everyone has of it but rather allows for multiplying ad infinitum the number of people who hold this knowledge. The economist expresses this strange characteristic by qualifying knowledge as being non-rival, meaning that users of knowledge are not rivals. In other words, the use of knowledge by an additional agent does not require producing an additional copy. The marginal cost of using existing knowledge is almost nil (especially in light of the new information and communication technologies). Therefore, the sharing and circulation of knowledge are actions that make no one poorer, but on the contrary enrich the communities in which they occur.