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Knowledge Acquisition via Three Learning Processes in Enterprise Information Portals: Learning-By-Investment, Learning-by-Doing, and Learning-from-Others1

Chungsuk Ryu¹; Yong Jin Kim²; Abhijit Chaudhury³; H. Raghav Rao⁴

¹ School of Management, State University of New York at Buffalo, 306 Jacobs Management Center, Buffalo, NY 14260-4000, U.S.A. · ² School of Management, Binghamton University (SUNY), Binghamton, NY 13902-6015, U.S.A. · ³ Department of Computer Information Systems, Bryant College, 1150 Douglas Pike, Smithfield, RI 02917, U.S.A. · ⁴ School of Management, State University of New York at Buffalo, 325 Jacobs Management Center, Buffalo, NY 14260-4000, U.S.A.

An enterprise information portal (EIP) is viewed as a knowledge community. Activity theory provides a framework to study such a community: members of an EIP conduct specific tasks that are assigned through a division of labor. Each member of an enterprise information portal can undergo three distinct types of learning processes: learning-by-investment, learning-by-doing, and learning-from-others. Through these three types of learning processes, each member achieves specialized knowledge that is related to his or her own task. Cumulative knowledge resulting from the learning processes is considered in terms of two distinct attributes: depth and breadth of knowledge. This paper formulates a mathematical model and defines the goal of an EIP member as maximizing the net benefits of knowledge resulting from individual investment and effort. Numerical examples are provided to analyze patterns of optimal investment and effort plans as well as the resulting accumulated knowledge. The results provide useful managerial implications. In business conditions characterized by high interest rates or high internal rate of returns, it is preferable for members to delay spending their resources for learning. Intensive investment and efforts to obtain knowledge are preferable when the discount rate of costs is high, when knowledge is durable, when the value of knowledge is high, when the initial level of knowledge is high, when the productivity of the learning process is high, and when sufficient knowledge is transferred from other members. On the other hand, the size of the EIP has a positive or negative effect depending on the attribute of knowledge and the productivity of learning processes. Further properties of the optimal decisions and learning processes are analyzed and discussed.

DOI

10.2307/25148679

Volume

29 (2)

Pages

245-278

Language

en

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