Management Benchmark Study-Book Summary Chapter 05-Literature, Summaries of Benchmarking

Download Management Benchmark Study-Book Summary Chapter 05-Literature and more Summaries Benchmarking in PDF only on Docsity! Ch 5 Knowledge Management 06.10.02.doc 06.10.02 Chapter 5. Knowledge Management1 By Kathryn A. Baker and Ghuzal M. Badamshina Knowledge management has become such a hot topic that it has been dubbed the business mantra of the 1990s (Halal 1998). The literature primarily addresses the growing importance of knowledge management for private sector organizations, but clearly knowledge-generating organizations such as federal science management and research agencies can not only benefit from this literature but also play a leadership role in furthering theory and practice in this area. Although these knowledge-oriented organizations have been in the business of creating and furthering knowledge development, they have not necessarily developed and articulated a systemic approach to knowledge management. This is a critical omission that should be corrected. Of all the management topics of potential relevance to public science organizations, this may be one of the most useful areas to pursue. Knowledge management is central to public science organizations. Although knowledge management has become a highly prominent topic, the term remains rather ambiguous and controversial, impeding progress in articulating what knowledge management entails and what knowledge-based organizations will look like. Many have questioned whether knowledge management is, or will ever become, a useful concept with practical application; others proclaim it is already the pivotal driver of organizational success and will only become more important in the future. The latter point of view is persuasive, but there is a long way to go in clarifying and articulating the concept of knowledge management. The belief that knowledge management is destined to become the key to future economic success is based on the following logic: 1. Many prominent scholars note that a new economic era, referred to as the knowledge-based economy, is already underway. In this new economy, knowledge is the source of wealth. It is assumed, therefore, that knowledge management will be the new work of organizations. 2. Knowledge management represents a logical progression beyond information management. Information technologies, at long last, have demonstrated a notable impact on organizational performance. Many believe that the next generation of information technology/artificial intelligence (IT/AI) products will increasingly enable knowledge management, in contrast to information management, and, as such, will have a far bigger impact on organizational performance (Sveiby 1997). 3. Knowledge management can also be seen as representing a culmination and integration of many earlier organization development ideas (e.g., total quality, reengineering, organizational learning, benchmarking, competitive intelligence, innovation, organizational agility, asset management, supply chain management, change management, etc.). It encapsulates these concepts into a larger, more holistic perspective that focuses on effectively creating and applying knowledge (Amidon 1998:47). This chapter begins by examining two primary and fundamental questions: ♦ What is the knowledge-based economy? ♦ What is knowledge and how does knowledge function as the source of wealth in the knowledge-based economy? 1 Related chapters include: Science Policy; Strategy; Change Management; Competencies; Innovation. Ch 5 Knowledge Management 06.10.02.doc 2 06.10.02 Only then does it address “What is knowledge management?” -- proposing a holistic view of knowledge management that can be applied to both private and public sector organizations. It then discusses how knowledge management could be used to improve science management in the public sector. This approach is driven by the following observations and suppositions: 1. There is a critical lack of vision in most of the knowledge management literature that stems from the fact the knowledge management discourse is often divorced from any real understanding of the role of knowledge in the knowledge-based economy and the actual dynamics of this new economy. 2. Too often what is discussed under the rubric of knowledge management is merely information management. 3. To fulfill the promise of knowledge management, a knowledge vision and strategy is needed that addresses how work systems will be transformed in the knowledge-based economy and how these transformed work systems will, in turn, transform firms, markets, and our economy as a whole. To reach this vision requires a better understanding of both the knowledge-based economy and the role of knowledge in this economy. A better understanding of knowledge management as it applies to private sector organizations may help to improve knowledge management in public sector science organizations and vice versa. What is the Knowledge-Based Economy? Classical economists have characterized economic history as consisting of distinct eras that correspond to shifts in the dominant source of wealth from land to labor to capital. In the 1980s, several prominent theorists, particularly Paul Romer (see Kelly 1996), Machlup (1980-1984), and Drucker (1988), predicted the rise of a new economic era in which knowledge would become the primary source of wealth (see Figure 1).2 Figure 1. Economic Eras Based on Changes in the Primary Source of Wealth Knowledge is clearly the primary source of wealth in the high-tech industries (such as the computer and software industries) and other knowledge-intensive industries (such as 2 In these transitions, the earlier sources of wealth do not disappear but they do become secondary. Ch 5 Knowledge Management 06.10.02.doc 5 06.10.02 partnerships between suppliers, developers, manufacturers, distributors, and customers (Halal 1998). Badaracco goes so far as to suggest that organizations will eventually be transformed into fluid networks of alliances and partnerships oriented toward creating, sharing, and applying knowledge. Alliances between suppliers, developers, manufacturers, distributors, and customers will blur the distinction between firms and markets, as well as the distinction between external and internal markets. These scholars begin to provide a picture of what the knowledge-based economy will entail, but many questions remain. For example: ♦ Do the facts that knowledge is an infinite resource and that there will be a lack of scarcity in the new economy suggest that competition will eventually disappear or will competition become more intense, as some have argued? Although knowledge, in theory, is infinite, there are limiting factors. Knowledge is neither free, nor freely available. Acquiring and continually renewing knowledge can cost dearly in terms of both time and money and the availability of knowledge can be controlled and restricted. ♦ If competition does not disappear, will it be primarily oriented toward developing and delivering knowledge-infused products/services or competing in terms of innovative business concepts and models? ♦ If knowledge alliances and positioning within knowledge networks become critical to future economic survival, will these networks become the new competitive forces? ♦ Will these knowledge alliances become so fluid that there will no longer be any stable organizational entities as exist today and current notions of firms and markets will be transcended? What the knowledge-based economy will ultimately become is still very much a mystery. Neither a list of organizational attributes nor the notion of a fluid network is sufficient to clarify how the organizational entities of this new economy will actually look and function. The only thing that is widely accepted is that the knowledge-based economy will be radically different. The fuzziness of the future does not preclude organizations from transitioning to knowledge- oriented enterprises, but it can make this transition more difficult. Actually, the degree of future uncertainty makes it all the more critical for organizations to have knowledge management systems in place to enhance their ability to successfully address this unknown future. Having a smart vision of how they should evolve in this knowledge-based economy can provide organizations with a competitive advantage. This vision is likely to change and improve over time but firms must begin the process of intelligently grappling with their uncertain but rapidly unfolding future prospects. This chapter posits a vision of the knowledge-based economy that focuses on how the organization of work will be transformed. It seems clear that the organization of work will be radically transformed, just as (or more so than) it was in the prior economic transitions. This vision proposes that work systems will become increasingly embedded in knowledge systems. Eventually, these work systems may no longer exist in organizations as we now think of them. As knowledge systems become more critical and prominent and work increasingly becomes embedded within them, the knowledge systems may become more important organizing entities than the initial organization entities that gave rise to them. Thus, knowledge-based enterprises may become more like knowledge system coalitions (similar to Badaracco’s knowledge alliances). Knowledge system coalitions may compete with one another and/or continue to build cooperative networks. They may be more or less fluid than organizations today. Individuals may Ch 5 Knowledge Management 06.10.02.doc 6 06.10.02 compete for membership in these knowledge system coalitions in order to enhance their ability to access to projects and to become part of winning project teams. In addition, knowledge system coalitions may need to compete for the best persons by giving them incentives to be exclusive to the particular knowledge system coalition. Whether or not this long-term vision of the evolving knowledge-based economy is correct, it is clear that, in the near-term, building knowledge systems and embedding work systems within knowledge systems will be an emerging economic reality. Organizations as we now know them may continue to exist for some time, but in their effort to construct and manage knowledge systems, they will increasingly connect and network with other organizations. (See Chapter 9 for a further discussion of this concept.) What is Knowledge and How does Knowledge Function as the Source of Wealth in the Knowledge-Based Economy? Though many of the early theorists (such as Drucker) used the terms information economy and knowledge economy interchangeably, the distinction between knowledge and information is now strongly emphasized. As a preamble to defining knowledge management, many begin by defining knowledge in a way that clearly distinguishes it from information. But differentiating knowledge from information does not go very far in clarifying what is meant by knowledge or knowledge management. Knowledge is not a unitary concept: there are many forms of knowledge. There are attempts in the more recent knowledge management literature to differentiate types and levels of knowledge. Some suggest a need to go beyond the concept of knowledge to address knowledge systems or ontologies in order to understand the full potential impact of knowledge. To make things more difficult, it is not enough to define knowledge; to be effective, managers must understand how knowledge functions in the knowledge-based economy and how exactly it creates or adds value. What is Knowledge? Knowledge versus Information Knowledge, information, and data are often represented as having a hierarchical relationship. Knowledge Information Data Data are discrete, objective facts about events or objects. Data become information when sorted, analyzed, and displayed in a manner that enables communication via language, graphs, or tables (Davenport and Prusak 1998). Dixon (2000:13) adroitly notes that information is data “in formation.” Tiwana (2000), using a catchy alliteration, says information is data that have had value added by having been contextualized, categorized, calculated, corrected, and/or condensed.4 4 Some might argue that some of these transformations, such as contextualization, would blur the distinction between information and knowledge. For example, Quinn et al. (1996) define knowledge as contextualized information. Ch 5 Knowledge Management 06.10.02.doc 7 06.10.02 Knowledge is far more difficult to define and its relation to information far more complex. Some argue that knowledge involves the link people make between information and its potential applications and, as such, knowledge is closer to action than either information or data (Dixon 2000; Davenport and Prusak 1998).5 This definition of knowledge corresponds to what many now label competence. Because knowledge has so many connotations, Sveiby (1997) prefers the term competence. Competence is the capacity to act effectively and efficiently and, according to Sveiby, it is the best way to describe knowledge in the business realm. But many do not confine their definition of knowledge as providing the basis for intelligent action. Knowledge can involve highly abstract cognitive understandings of phenomena that do not necessarily have clear practical applications, at least not in the immediate term. These two views of knowledge parallel the artificial distinction between applied and basic science, a distinction that has been losing ground as applied knowledge is becoming more complex and as private companies and universities are increasingly collaborating to pursue both forms of knowledge. This distinction between applied and more abstract knowledge is actually a continuum and does not go far enough to explicate the role of knowledge in organizations or in the knowledge-based economy. Moreover, both types of knowledge are important to organizations today. Basic fundamental knowledge or science often is essential for promoting innovative research and development (R&D); applied knowledge is thought to be important to promote efficient and effective organizational operations. A better understanding of the levels and types of knowledge may be necessary to understand the role of knowledge in the knowledge-based economy. Levels of Knowledge Knowledge can be seen as occurring at various levels. For instance, knowledge can exist at lower, practical levels (close to action) as well as at higher, theoretical levels (focused on high level understandings that, as yet, have little relation to practical action). A common way of characterizing levels of knowledge is to see knowledge as progressing from identifying attributes of concepts, to establishing relationships between concepts, to specifying the conditions under which these relationships apply. A similar view characterizes knowledge as progressing from relational thinking to systems thinking and, within systems thinking, as progressing from identifying system characteristics, to detecting system trends, to explaining system dynamics. Nonaka and Takeuchi (1995) see knowledge as moving from lower level, general forms to higher level, more precise forms (for example, from simple slogans, to similes and metaphors, to systematic analogies, to structured models and theories). Lower level knowledge (slogans, similes, and metaphors) provides insightful, albeit imprecise, understandings that can help generate higher level, more systematic and explicit knowledge (analogies and, eventually, highly structured and precise models and theories). Distinguishing lower from higher levels of knowledge may also equate to distinguishing between discrete knowledge elements or statements versus knowledge systems. Going beyond knowledge elements to build knowledge systems can be seen as a qualitatively higher level of cognitive activity. Knowledge systems can be ontological systems, frameworks, theories or models that not only show relationships, suggest connections, facilitate comparisons, and predict consequences but also can be used to interpret and incorporate new experiences and information. They can involve dynamic, on-going processes that involve seeing and categorizing existing patterns and 5 Sveiby (1997) also sees knowledge as closer to action than information but he also sees knowledge as action. Knowledge is the act of knowing and involves learning, forgetting, remembering, and understanding. Information, on the other hand, is not action. Ch 5 Knowledge Management 06.10.02.doc 10 06.10.02 knowledge that is not yet fully articulated and systematized in the minds of individuals – such as notions, impressions, experiences and cumulated wisdom – and, as such, is difficult to explicitly document. In this sense, tacit and explicit knowledge can be seen as a continuum ranging from more or less tacit (or more or less explicit). Although capturing tacit knowledge and converting it to explicit knowledge and vice versa may constitute an essential source of knowledge creation, tacit to tacit knowledge conversions and exchanges are likely to extend far beyond socialization. Persons sharing what they know and, especially, struggling together to further develop and systematize what they do not yet explicitly know, is not adequately captured by the notion of socialization. This interpersonal exploration and development of tacit knowledge, which often can be explicitly articulated only after considerable effort, is generally seen as the primary source of new knowledge creation (Sveiby 1997). The Value of Knowledge in the Knowledge-Based Economy Malhoutra (2000) suggests that data, information, and even knowledge often have little value. Newspapers, periodicals, and knowledge-oriented web sites typically do not make money by selling their knowledge content to consumers; they make money by selling advertisement space to others who want to disseminate particular information to these consumers. The key is to determine what makes knowledge valuable and, in particular, how knowledge creates wealth in the new knowledge-based economy. While some suggest that infusing knowledge into products and services is what makes knowledge valuable, a stronger contention is that building the knowledge systems that allow for product and service innovation is the key to creating value and wealth.8 In addition, knowledge systems that inform business concepts/models as well as those that inform operational processes are also critical. Basically transforming work systems at all levels by embedding them within appropriate and effective knowledge systems is what adds value and creates wealth for the organization. Many work systems require complex, multidisciplinary knowledge systems to promote effective decision-making and action: scientific work systems constitute an excellent example of this. Constructing knowledge systems, facilitating links between diverse knowledge systems, and embedding work systems within knowledge systems should become increasingly important topics in knowledge management. If knowledge systems are based on a common ontological structure, higher level ontologies can be abstracted across diverse knowledge domain ontologies to support dialogue and scientific exploration across these knowledge systems. These abstracted ontologies can be designed to support links ranging from superficial to more sophisticated levels. Constructing inter-relating knowledge systems and embedding work systems within these knowledge systems is the likely key to the future of knowledge management. What is Knowledge Management? The term knowledge management was first introduced in a 1986 keynote address to a European management conference (American Productivity and Quality Center 1996). This term had immediate and vast appeal and, at the same time, spawned strongly felt criticism. 8 The key to the knowledge-based economy is not knowledge-infused products but tacit knowledge that provides the capacity for these knowledge-infused products and for non-codified knowledge services (Sveiby 1997). Ch 5 Knowledge Management 06.10.02.doc 11 06.10.02 A Critique of Knowledge Management The major criticisms of knowledge management are that: ♦ It has traditionally conjured up too close an association with information management and information technology (IT). ♦ It implies that knowledge can be managed. ♦ It tends to be so broad and vague as to have little meaning. ♦ It tends to focus on the nuts and bolts of knowledge creation, capture, sharing, use and reuse, rather than providing a true vision and strategy that conveys how knowledge-based enterprises will function and succeed in the new knowledge-based economy. In addition, more specific criticisms have been leveled at particular views of knowledge management. The most common type of definition describes knowledge management as a set of processes directed at “creating-capturing-storing-sharing-applying-reusing” knowledge (Sydanmaanlakka 2000). This type of definition is criticized for making knowledge management appear to involve somewhat mechanistic and sequential process steps and for focusing attention on explicit knowledge artifacts as opposed to tacit knowledge. Knowledge engineering reflects this view of knowledge management. A definition with similar problems sees knowledge management as “delivering the right knowledge to the right persons at the right time.” This definition emphasizes explicit knowledge artifacts over tacit knowledge and ignores knowledge creation. Alternative definitions have been proffered that attempt to better capture the complexities of knowledge and knowledge management. For example, Snowden (2000) defines knowledge management as: The identification, optimization, and active management of intellectual assets, either in the form of explicit knowledge held in artifacts or as tacit knowledge possessed by individuals or communities. The optimization of explicit knowledge is achieved by the consolidating and making available of artifacts. The optimization of tacit knowledge is achieved through the creation of communities to hold, share, and grow the tacit knowledge. The active management of intellectual assets is the creation of management processes and infrastructure to bring together artifacts and communities in a common ecology that will sustain the creation, utilization and retention of intellectual capital. This definition, though a bit cumbersome, recognizes that knowledge management must address both explicit and tacit knowledge, as well as the interaction between the two, and begins to address some of the mechanisms for doing this. It does not, however, capture all aspects of knowledge management, nor does it address how knowledge will be used or how a knowledge-based enterprise will ultimately function and/or look. The problems with the term knowledge management can be overcome if one thinks of knowledge management as building and enhancing knowledge systems and embedding work systems within these knowledge systems, rather than managing something as nebulous as knowledge per se. Thus, an appropriate definition of knowledge management would be creating knowledge-rich environments and knowledge-rich interactions in the conduct of work. More specifically, knowledge management is developing and managing integrated, well-configured knowledge systems and increasingly embedding work systems within these knowledge systems. Ch 5 Knowledge Management 06.10.02.doc 12 06.10.02 Defined in this way, knowledge management does not over-emphasize IT. It is clear that both knowledge systems and the processes of embedding work systems within knowledge systems can be managed. Finally, this definition is broad enough to capture all aspects of knowledge management but is not overly vague – one can define, with some precision, what a knowledge system is. One can also articulate how work systems can become embedded within knowledge systems. In addition, more specific knowledge systems and corresponding work systems can be specified for particular contexts. While an organization may vary in the extent to which it develops full-fledged and integrated knowledge systems and embeds work systems within these knowledge systems, all organizations need to direct greater attention to assessing and improving their knowledge systems and linking work processes to these knowledge systems. However, this definition does overly attend to the nuts and bolts of knowledge management to the point of ignoring the bigger picture. It leads to an enterprise-wide vision – a view absent in the literature and in organizations, although there is a recognized need for both vision and strategy. The vision of building knowledge systems and embedding work systems within them encourages the whole spectrum of on-going, dynamic, interrelated knowledge-oriented activities to be taken into consideration, while making it impossible to reduce knowledge management to a set of discrete, mechanistic knowledge management practices. This view of knowledge management enables the organization to identify its critical knowledge domains, its most immediate and future knowledge priorities, goals and objectives, and to work toward building critical knowledge systems and embedding work systems within them. Finally, it helps the organization identify the most appropriate set of knowledge management practices, determine how information technology (IT) and artificial intelligence (AI) can best enable these well-configured, integrated enterprise- wide knowledge systems and embed work systems within them. A Critique of the Practice of Knowledge Management Backlash to the term knowledge management seems not to have arrested the growing surge of interest in and adoption of knowledge-oriented practices by organizations. However, the practice of knowledge management suffers from the same problems as the literature. So-called knowledge management practices are often little more than renamed information management. Even though the knowledge management literature now clearly stresses the difference between information and knowledge, knowledge management practices often fail to follow suit. Knowledge management activities have typically been directed at the nuts and bolts of knowledge management, as opposed to developing a vision and strategy for knowledge management. The American Productivity and Quality Center conducted the first major knowledge management benchmarking study in 1996. This study found that knowledge management was a highly recognized and prominent term, that it was becoming a major consulting thrust for several prominent international consulting companies, and that companies in all sectors had initiated a variety of knowledge management activities. A more recent survey of 200 senior executives (described in Hackett 2000) found that: ♦ 80 percent of the senior executives reported that they had some knowledge management efforts underway ♦ 25 percent had a chief knowledge management officer or chief learning officer (though half were not supported with a dedicated budget or staff) ♦ 21 percent had an articulated knowledge management strategy Ch 5 Knowledge Management 06.10.02.doc 15 06.10.02 not easy, to identify and distinguish between knowledge that needs to be preserved and shared and that which is no longer useful and should be discarded. Discarding obsolete information and unlearning certain practices is as much a part of knowledge management as is creating and capturing new knowledge. As John Maynard Keynes once said, “The greatest difficulty lies not in persuading people to accept new ideas, but in persuading them to abandon old ones.” Leonard-Barton (1995) and Christensen (1997) both discuss the notion of core capabilities becoming core rigidities. Knowledge landscapes and other conceptual tools can greatly aid in determining the types of knowledge communities, networks, and alliances that will produce the greatest value. European companies have demonstrated particular interest in knowledge analysis and mapping. Toward an Enterprise-wide Knowledge Management Vision and Strategy Identifying various knowledge management practices is not adequate guidance to companies interested in promoting and fostering their knowledge capability. A holistic enterprise-wide vision and strategy are needed to meet this need. But as noted, integrated, holistic approaches for building and managing knowledge-based enterprises are largely absent not only in practice but in the literature. Some theorists have discussed what an enterprise-wide approach to knowledge management would have to entail – such as an overall knowledge-oriented vision, strategy, culture, processes, infrastructure, and structure (Morris 1999; Tissen et al. 1998; Devlin 1999), but they fall short of actually proffering a concrete, holistic model. Figure 4 delineates the critical elements of a holistic and integrated knowledge management model and shows how these elements fit together. This model moves beyond the basic ideas of vision/strategy, leadership, measurement and analysis, resources and infrastructure, structure and processes to elaborate what is entailed in each of these areas and to provide a visualization of a holistic knowledge management model. The model in Figure 4 focuses on actual tangible elements of a holistic approach to enterprise knowledge management, in contrast to the intangible knowledge conversion processes (socialization, internalization, externalization) that Nonaka and Takeuchi (1995) emphasize. These tangible elements include: ♦ Specified knowledge goals, objectives, priorities ♦ Transformation plans to transition from “as is” to “to be” ♦ An articulated knowledge landscape ♦ Measures and assessments of the state of knowledge and the knowledge management system ♦ Knowledge leaders, advocates, activists, and facilitators (these persons will be assigned to various communities of practice, knowledge communities, innovation initiatives and projects; they will be in charge of developing and maintaining knowledge networks; they will be responsible for further articulating of the knowledge landscape and for measuring and assessing the state of knowledge and the knowledge system) ♦ Knowledge-oriented IT, AI, and communication technologies (CT) ♦ Tacit knowledge assets as represented by actual human beings (internal staff and external collaborators) and established processes to facilitate interactions between them ♦ Explicit knowledge assets as represented by enterprise information systems, enterprise knowledge systems, databases, IP, and other knowledge artifacts ♦ Competitive intelligence and benchmarking activities Ch 5 Knowledge Management 06.10.02.doc 16 06.10.02 Figure 4. Integrated Knowledge Management Model: Components and Linkages Knowledge System Vision & Strategy (Direction, Leadership, Processes, Mechanisms, & Incentives to Capture, Absorb, Share & Create Knowledge Utilizing/ Leveraging Knowledge Knowledge System Infrastructure & Technologies Knowledge Leaders/Advocates/ Activists/Facilitators Communities of Practice, Knowledge Communities, & Knowledge Networks Transformation Plan As Is –>To Be Intra-/Inter-Organizational Competencies Knowledge Goals, Objectives, Priorities Articulated Knowledge Landscape (mapping, visualization, analyses) Knowledge Measurement & Assessment Tacit Knowledge Assets/Resources Staff recruitment, development, interactions, relationships Explicit Knowledge Assets/Resources Enterprise information & knowledge systems, databases, IP and other artifacts Competitive Intelligence, Benchmarking R&D & Innovation Initiatives/Programs/Projects Idea Solicitation, Selection, Prioritization, and Implementation Processes Information Technology, Artificial Intelligence, & Communication Technology KM Outcomes Smart Processes; Knowledge-Infused Products & Services; Creative Business Concepts; Critical Knowledge Systems; Work Systems/Networks embedded within Knowledge Systems Ch 5 Knowledge Management 06.10.02.doc 17 06.10.02 ♦ Communities of practice, knowledge communities, and the knowledge partnerships and alliances comprising knowledge networks ♦ Intra- and inter-organizational competencies ♦ R&D and innovation programs, initiatives and projects ♦ Knowledge management outcomes, including smart, knowledge-infused processes; knowledge-infused products and services; creative business concepts; critical knowledge systems; and work systems embedded within knowledge systems. It is these tangible components of knowledge management that give rise to the important intangible attributes – such as the level, range, and depth of tacit knowledge, individual competencies, intra- and inter-organizational competencies, a knowledge-oriented culture, knowledge leadership, knowledge socialization, internalization, and externalization. Focusing on the tangible components helps knowledge management become a reality as opposed to a vague concept that is difficult to grasp and put into practice. The human elements of the enterprise knowledge management system provide critical feedback opportunities. Knowledge leaders, advocates, activists, and facilitators, tacit assets (staff in general), members of the various communities of practice, knowledge communities, and key network development staff, key R&D staff, and innovation program and project staff are all critical sources of input and feedback for improving and advancing the enterprise knowledge management system. Stages of Knowledge Management There has tended to be a progression of knowledge management goals and stages: ♦ Stage 1—Smart Processes: Knowledge management activities often initially focus on improving processes (focusing on continuous improvement through lessons learned, best practices, process innovation, getting the right information/knowledge to the right people at the right time, etc.). Many e-business initiatives are merely speeding up existing processes by enhancing the flow of information and data, such as electronic ordering, providing product and service information and support over the internet, and promoting just-in-time delivery. These process-oriented improvements can eventually focus on developing more knowledge-infused, smart processes. For example, the ordering process can assist the customer in more exactly determine the product(s) needed and estimate the amounts required for a particular project. ♦ Stage 2—Knowledge-Infused Products and Services: The focus next turns toward creating new and increasingly knowledge-infused products and services (with an emphasis on enhancing creativity and more effective and efficient R&D). ♦ Stage 3—Innovative Business Concepts: Attention, at least in the literature, has most recently been directed at developing new business concepts (changing the rules of the game and the game itself). ♦ Stage 4—Constructing Critical Knowledge Systems and Conjoining Work Systems with Knowledge Systems: The ultimate goal of knowledge management is to construct and continually enhance knowledge systems and to conjoin knowledge systems and work systems. All levels of work should be embedded within the appropriate knowledge systems, including strategic decision-making, operations, R&D, engineering, maintenance, marketing, etc. Building better and better knowledge systems and conjoining work systems with these knowledge systems is the on-going motor of innovation. The challenge is to determine what knowledge systems are critical to the various work systems and constructing these to facilitate and improve work system Ch 5 Knowledge Management 06.10.02.doc 20 06.10.02 The Application of Knowledge Management to Public Science Management Most knowledge management issues are as applicable to the management of public sector organizations as to the management of private sector enterprises. In fact, it would seem that traditional knowledge-oriented organizations, such as universities, research and development laboratories and public sector science funding and directing organizations, should play a lead role in developing and furthering the theory, practice, and tools to promote better knowledge management. Although there is substantial overlap across types of organizations, the particular knowledge management challenges facing public science funding and directing organizations will differ some from those confronting public science executing organizations, and in both cases these challenges should differ somewhat from those of greatest concern to private sector enterprises. The biggest knowledge management challenges for public sector science funding and directing organizations might include: ♦ Mapping and assessing various knowledge domains (recognizing that these domains often span the traditional disciplines) to determine where knowledge gaps/needs exist as input to establishing research agendas and funding priorities ♦ Having the knowledge to effectively inform the strategic direction of scientific research advancements toward solving our most critical health, security, environmental, and social problems ♦ Promoting collaboration among various science funding and directing organizations (primarily but not exclusively those in the public sector) to achieve greater efficiency, effectiveness, and synergy among these organizations as well as among science performing organizations ♦ Enabling the capture and sharing of knowledge across science organizations, particularly among public sector science organizations ♦ Balancing the goals of sharing knowledge among public science organizations and demonstrating organizational performance accountability by claiming credit for new scientific developments ♦ Developing effective and useful knowledge systems to deliver knowledge to various user groups and to both general and specific stakeholders—these knowledge systems can be used to provide interested persons with information regarding the organization’s performance accountability and the benefit of the public money expenditures, to allow interested persons to obtain information on recent scientific breakthroughs and their import or to identify the state of knowledge in particular areas, etc. ♦ Promoting partnerships between the public sector research and development laboratories, private sector research and development efforts, and universities to enhance knowledge creation and share the cost/risk of major science initiatives ♦ Promoting international scientific partnerships and global scientific advancement; ♦ Promoting the utilization and commercialization of publicly funded science ♦ Encouraging and facilitating good knowledge management practices in public sector, as well as private sector, science executing organizations; and providing direction in building knowledge-rich environments and knowledge-rich interactions in the conduct of science. Ch 5 Knowledge Management 06.10.02.doc 21 06.10.02 The biggest knowledge management challenges for public sector science executing organizations might include: ♦ Enhancing their capacity to identify and become leaders in cutting-edge science ♦ Creating knowledge-rich environments and knowledge-rich interactions to promote the conduct of science ♦ Developing an effective science portfolio and an effective pipeline of projects, recognizing the tradeoffs between overlap and efficiency ♦ Facilitating the proprietary capture of new science developments/discoveries as intellectual property ♦ Determining how to develop knowledge systems that adequately capture the state of knowledge in various scientific domains and can be easily utilized ♦ Determining how to make links across diverse knowledge systems ♦ Balancing the goals of generating intellectual property and sharing knowledge freely to advance rapid and effective scientific development ♦ Identifying and managing critical knowledge competencies and assets ♦ Expanding their knowledge-base and critical competencies through strategic partnerships/alliances ♦ Establishing processes to better foster knowledge creation and innovation. Science performing organizations can basically apply the holistic knowledge management model in Figure 4, except that the goals may differ somewhat. Instead of embedding work systems within appropriate knowledge systems, the goal in federally funded research organizations would be to embed the conduct of science and scientific decision-making within appropriate, well- designed science knowledge systems. The knowledge management system required by science directing and funding organizations might be somewhat more restricted. They may only need knowledge systems that enable their ability to assess, evaluate, and inform strategic decision- making that will contribute to bringing about a more effective and efficient national science system. These latter organizations, however, need to encourage or require knowledge dissemination and knowledge systems as part of funded research. Unlike the organizational transformations characterizing prior major economic shifts, the transition to the knowledge-based economy will indubitably be faster and will exert intense pressure on organizations to take charge and stay ahead of the competition. For this reason, organizational transformation will need to be directed and facilitated, rather than a slow emergent phenomenon, as in the case of these earlier economic transformations. This does not mean that organizations need to implement a full-blown knowledge management system all at once, but they must aggressively promote and direct the progressive formation and continual improvement of this system. References American Productivity and Quality Center. 1996. Knowledge Management Consortium Benchmarking Study. Houston TX: American Productivity and Quality Center. Amidon, Debra. M. 1998. The Evolving Community of Knowledge Practice: The Ken Awakening. International Journal of Technology Management, 16(1-3):45-63. 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