Knowledge management, social learning, and options to learn

by 1975- Wen, Fang-I

Knowledge plays an important role in firm decision making as it involves the selection of technological systems and understanding and executing technologies that are implemented. Knowledge management therefore is a learning behavior which is undertaken to acquire additional knowledge and improve the firm’s profit capacity. The core elements of knowledge management involve i) the learner, to whom the learning behavior is attributed, ii) the learning process, which consists of acquiring information and processing information to additional knowledge, and iii) the learning outcome, which is what we obtain from knowledge management. This research study focuses on the firm decision maker as the learner while the learning outcome is the updated knowledge base playing an important role in the firm’s decision making. The learning process, where the learner obtains additional knowledge, has two phases. In the information acquisition phase, the decision maker acquires internal information by managing the internal data from past experience (for example, learningby-doing), and/or acquires the external information by communicating with others via social learning activities (e.g., conversation, cooperation, and collaboration). On the other hand, the knowledge updating phase involves the use of a learning mechanism translating the collected information into additional useful knowledge feeding into the existing knowledge base. This research addresses i) how the learner (the firm decision maker) learns and seeks to formalize the learning process, ii) how the decision maker chooses among different knowledge management schemes, and iii) how social learning behavior reflects on iv production heterogeneity. This thesis research develops a conceptual model focusing on the definition of knowledge, the different ways of executing learning process, and the way the updated knowledge base influences future decision making. The theoretical model is investigated by using a mathematical model where the decision maker maximizes the firm’s profit over time under production and knowledge management constraints. The optimization conditions point out the marginal costs and benefits of learning and guides the decision maker to allocate the physical input and the effort for knowledge management. Learning strategies, such as always learn, wait to learn, learn-in-bursts, and quit learning, are observed in the deterministic dynamic programming model as the output price changes. Considering the learning decisions under uncertainty, two stochastic dynamic programming models are constructed where the market and technological uncertainties are represented by the stochastic properties of output price and knowledge base accumulations. The numerical results indicate that the decision maker faces several possible states because of the market and the technological uncertainty. In addition, each state has its corresponding decision, and the decision maker will not make the learning decision until the true state is revealed. The empirical model is used to reveal the connection between social learning and production heterogeneity. A latent class stochastic frontier model (LCSFM) is introduced to estimate the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) India data. The households’ group-memberships are obtained, and the households assigned to the same group are assumed to use the same production technology. Thus, the common characteristics of the households in the same group v indicate the reason behind technical heterogeneity across households. The empirical results indicate that caste rank plays an important role in households’ production decisions. Since households in the same caste rank are more likely to communicate with each other, they have a greater opportunity to exchange production information. The frequent social activities within the caste rank provide the opportunity for social learning. Thus, the importance of caste rank in production behavior represents the importance of social learning in production decision. vi