The following table looks at both the major performance management processes and knowledge management processes:
Knowledge Identification and Capture
Knowledge management and performance management are intertwined. In An Intelligent Organization: Integrating Performance, Competence, and Knowledge Management (John Wiley, 2002), written by the Nokia's former director of human resources, Pentti Sydanmaanlakka, key objectives for an organization include the continuous improvement of performance and competence, and the continuous application of new knowledge. In Larry Pederson's book, Performance-Oriented Management: A Guide for Government Agencies (Management Concepts, Inc., 2002), the importance of making a smooth transition from a management philosophy that is reactive and task-oriented to one based on a vision of accomplishment (i.e., performance improvement) is stressed. The strong linkage between performance management and knowledge management is further evidenced by Ernie Chen, a corporate knowledge strategist at JT Frank Management Center, who discusses knowledge management as the key to sustainable performance. Chen states that strategic knowledge management is about creating an innovative culture supported by collaborative technologies to secure competitive advantage and sustainable performance and to enhance productivity by leveraging on knowledge (http://www.kwx.com.my/kwx/asp/articles02/articles_0206.asp). Chen indicates that knowledge professionals are open-minded performance-oriented professionals that have the passion to develop the eight most innovative skills for tomorrow's knowledge economy: strategic thinking, knowledge responsibility, performance-directed learning, contribution in innovative teams, professional discipline, self-driven innovation habits, solution focused mindset, and personal knowledge creation.
It thus becomes clearer that knowledge management and performance management coexist and are integral parts of each other. As we delve deeper into the specific knowledge management and performance management processes previously mentioned, a number of parallels appears among these processes. First, a closed-loop system exists for both performance management and knowledge management life cycles. In the performance management life cycle, planning is initiated that leads directly to monitoring, developing, rating, and rewarding, and then back to planning. First, goals, measures, and standards are established. Then, the performance is measured, which leads to addressing poor and good performance ("developing"). Summarizing the performance is then conducted ("rating"), and recognition and rewards are given for good performance ("rewarding"), which then leads into reviewing next year's goals, measures, and standards ("planning"), and the cycle continues.
Knowledge management also has a closed-loop system whereby knowledge is identified and captured, then shared with others. Once shared, the knowledge is applied, combined with other knowledge, and internalized by individuals, who then may create new knowledge. This new knowledge then needs to be captured, shared, applied, and the cycle continues.
A second parallel between performance management and knowledge management is that recognition and reward are important factors to help motivate certain desired behaviors. An annual performance plan provides the employee with the blueprint of what goals and activities are important for the employee to accomplish in a given year. If performance on these activities is done well, the employee is typically recognized and rewarded in some way. In a similar manner, extrinsic and intrinsic rewards are important for building a knowledge sharing culture. Intrinsic rewards may relate to self-satisfaction in sharing one's knowledge with others to see them learn. In academia, this is sometimes referred to as "psychic income" (versus "real income") as professors gain satisfaction in seeing their students learn. Extrinsic rewards are also used in knowledge management, but "name recognition" appears to be one of the most important ways to recognize and reward an individual or team. For example, if Jay Liebowitz's lesson in the organization's lessons-learned information system is the most accessed and frequently used lesson by others in the organization in a given month, Jay Liebowitz may be recognized by the organization in some way. Or if Jay Liebowitz is serving as the facilitator of an online community that is achieving tremendous growth and value-added benefits in the organization, he may be recognized or rewarded in some manner. From various studies, including McClure-Wasko's work at the University of Maryland, intrinsic rewards have a greater impact in knowledge management than extrinsic rewards; however, most organizations will use a combination of both.
A third parallel between performance management and knowledge management is that time spent up front in the stages of "planning" (the first step in performance management) and "knowledge identification" (the first step in knowledge management) will produce maximum benefits later in the life cycle. Setting proper goals and measures and establishing standards, as well as agreeing and communicating these elements to the employee, are critical first steps in achieving high performance. In the same manner, critical "at risk" knowledge areas need to be identified to see where knowledge gaps may result (due to experts leaving the organization with no backup) in the organization. These areas may then be identified as prime candidates for knowledge capture activities. A knowledge audit is typically conducted whereby areas are identified in which knowledge is missing or available, where expertise may be found, and the like. A knowledge map is then created to show where the pockets of expertise exist, and what are the links and relationships between individuals or departments in an organization. Social network analysis is often used to depict communication patterns and relationships between individuals or units in an organization. In both performance management and knowledge management, time well spent in the initial steps or processes should bear fruit in later stages of the life cycle. In the software programming milieu, if time isn't properly spent in the requirements stage, then this neglect usually leads to disaster in the encoding and testing stages.
A final parallel between performance management and knowledge management is that each area is built around a framework. In performance management, goal-setting theory is often used as a framework whereby it is hoped that good things will come to people who meet their intended goals. In knowledge management, a knowledge framework, as proposed in Figure 6.1, should first be developed in order to better understand how to share and manage knowledge.
Figure 6.1: Conceptual View of the Knowledge Framework.
In Figure 6.1 key components for decision-making include data, information, knowledge, and individual and organizational processes. Data is raw or discerned elements. When these elements are patterned in a certain way, data is transformed to information. Once certain rules or heuristics are applied to this information, knowledge is then created as actionable information for producing some value-added benefit. Here, knowledge is the capability to act—making information actionable. As knowledge is created and captured, learning takes place and the knowledge is applied and embedded within individual and organizational processes. The learning effect will then create new knowledge, which will then cycle through the data-information-knowledge-process transformation and iteration.
The key enablers of this knowledge framework, as defined by Jay Liebowitz and Isaac Megbolugbe, are the domain context, organizational culture, individual value system, benchmarking/standards, and management initiatives (Liebowitz and Megbolugbe, 2003). Knowledge must be applied in context in order to benefit from how it is structured or functions in that given domain. Knowledge is affected by the organizational culture as well as an individual's value system and worldview of the organization. In order for improved performance and measurement to take place, benchmarking and standards need to be created and applied (as is the case with performance management systems). Lastly, management initiatives, such as the technological infrastructure, will affect how knowledge is created, shared, and embedded within the individual, group, organization, and interorganization. Simply put, this knowledge framework seems to be consistent with Verna Allee's view of organizational intelligence in The Knowledge Evolution: Expanding Organizational Intelligence (Butterworth-Heinemann, 1997); she notes that for any system (or domain), we need to know how it is structured or functions, how it learns so it can grow or improve, and how it performs in relation to certain standards. This knowledge framework integrates the concepts of knowledge, learning, and performance together in a manner that enables us to account for organizational intelligence.
Takeuchi and Nonaka's framework for knowledge creation, as explained in their book The Knowledge Creating Company (Oxford University Press, 1995), can be used to connect one phase of the knowledge framework to another in a phase diagram that shows an array of planes in a trajectory extending outwards from the origin of a Cartesian graph (see Figure 6.2).
Figure 6.2: The Knowledge Spiral.
What Nonaka calls the knowledge spiral can be used as the conceptual mechanism for knowledge creation that causes the framework to move from one phase to another. The knowledge spiral (depicted above as "messy" circles due to a lack of standard transformation processes) reflects four modes of knowledge conversion that are created when tacit and explicit knowledge interact with each other. The four modes, called the "engine" of the entire knowledge creation process, are socialization, externalization, combination, and internalization. These modes, according to Takeuchi and Nonaka, are what the individual experiences; they are the mechanisms by which individual knowledge gets articulated and amplified into and throughout the organization. Socialization is a process of sharing experiences and creating tacit knowledge such as shared mental models. Externalization is a process of articulating tacit knowledge into explicit concepts (e.g., concept creation or as triggered by a dialogue). Combination is a process of systemizing concepts into a knowledge system, and involves combining different bodies of explicit knowledge. Internalization is a process of embodying explicit knowledge as tacit knowledge (e.g., learning by doing). As one moves through the knowledge levels of an individual, group, organization, and interorganization, these four modes are typically applied.
Nonaka's model is temporal, and Liebowitz-Megbolugbe's conceptual knowledge framework previously discussed is cross-sectional. By combining both Nonaka's and Liebowitz-Megbolugbe's models, the result is an integrated framework with a longitudinal view of a knowledge framework that is able to conceptually account for both creation and management of knowledge. An analogy is to view the knowledge framework as a globe. The globe can be shown to rotate on its axis. But the earth also revolves around the sun. This is the Nonaka model. When we integrate both perspectives at the same time, we are able to understand seasons. Seasons represent a metaphor for what may be called stable equilibrium.