Codification Strategies in Knowledge Management Processes – Learning from Simulation

Submission April 15, 2000 Codification Strategies in Knowledge Management Processes – Learning from Simulation 1 The Knowledge Age is upon us. We have moved past the Industrial Age and the Information Age, and are now venturing into a time when strategic managing of information and resources is an even greater critical success factor for organizations. Concurrent with this macro trend, there is a tremendous need for organizations to develop new business processes to better capture and utilize knowledge. Corporations are investing millions of dollars in formal and informal knowledge management (KM) systems, often without grounded theoretical rationale and with widely varying results. The formal study of KM is a relatively young but exciting field of inquiry. Over the past decade, numerous books and articles have been dedicated to the topic, but there is a significant gap in the literature related to empirical examination of KM theories. This paper provides data related to one of the key research topics in KM – codification strategies for capturing and sharing knowledge within organizations. With the assistance of a Stella Simulation Software Program, I replicate the KM processes of consulting firms and examine the performance of multiple KM codification strategies and related organizational design elements. The goal is to test the generally accepted theory that firms should select one dominant codification strategy (high or low), rather than attempt to adopt split (50/50) strategies. Key elements in the determination of the strategy include organizational size and knowledge specificity. I also examine the impact of other variables such as turnover, investments and incentive programs on the performance of the KM system. Theoretical Background and Hypotheses Although the study of KM has been around as long as there have been organizations, the formal study of KM and organizational design has exploded over the past decade. Peter Drucker (1988) set the stage with his description of the movement toward informationbased organizations and knowledge specialists. In the early to mid 1990s, much research was dedicated toward defining KM and describing its importance to competitive advantage (Badaracco, 1991; Nonaka and Takeuchi, 1995). For the purposes of this paper, I define KM as the formal design of systems within an organization to improve the development, capture and dissemination of knowledge. In late 1990s, researchers began to focus on detailed KM strategies and organizational design. Bierly and Chakrabarti (1996) stressed the importance of internal and external learning in their study of the U.S. pharmaceuticals industry. O’Dell and Grayson (1998) identified the importance of understanding knowledge transfer elements in organizations such as technology, culture, leadership and measurement. As the study of KM processes continued to grow in sophistication, the underlying nature of the knowledge in organizations came under investigation. The resulting paradigm posited tacit vs. explicit knowledge. Tacit knowledge is generally characterized as complex, hard to describe and resides in the minds of individuals while explicit knowledge is essentially the opposite – simple, easy to describe and captured in written form, databases or routines (Grant, 1996). Nonaka (1995) described multiple methods of converting the knowledge from tacit (individual) to explicit (organizational). The balance of tacit and explicit became a key issue. Codification Strategies in Knowledge Management Processes – Learning from Simulation 2 An excellent conceptual example of different tacit/explicit KM strategies was provided in the Harvard Business Review (Hansen et al., 1999). In a study of consulting firms, health-care providers and computer companies, the authors presented a theory that firms adopt either personalization (tacit) or codification (explicit) strategies – but they should not adopt both (50/50 splits). A personalization strategy is essentially a low codification strategy that results in technologies and incentives designed to facilitate person-to-person knowledge transfer. A codification strategy calls for a high codification infrastructure which results in more knowledge “reuse” via person-to-document exchange. Our research question addresses this particular issue and attempts to answer the question with empirical evidence – do different codification strategies result in a variance in performance? Specifically, our goal is to provide data to support or refute the following hypotheses: • H1: Firms will achieve better performance utilizing a dominant codification strategy (high or low codification) than firms utilizing a split (50/50) codification strategy. • H2: Large firms with high knowledge specificity will achieve better performance utilizing a high codification strategy. • H3: Small firms with low knowledge specificity will achieve better performance utilizing a low codification strategy.