Knowledge acquisition via three learning processes in enterprise information portals: learning-by-investment, learning-by-doing, and learning-from-others (1).(SPECIAL ISSUE)

Abstract

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.

Keywords: Knowledge management, enterprise information portals, learning, activity theory

**********

Introduction

There has been extensive research on knowledge management related to information technology (Alavi and Leidner 2001; Becerra-Fernandez and Sabherwal 2001; Earl 2001; Grover and Davenport 2001), but relatively little attention has been given to learning processes that form the critical part of knowledge acquisition in knowledge portals. This study focuses on knowledge acquisition processes and the individual user's investment strategy to obtain the optimal benefit from specialized knowledge in enterprise information portals (EIP). An EIP, in this study, is defined as a knowledge portal whose main function is to assist its members in obtaining specialized knowledge through various learning processes (Dias 2001; Mack et al. 2001). Further, an EIP can be considered to be a knowledge community (Bakos and Brynjolfsson 1997; Strader et al. 1998) that is composed of employees in a single company or in multiple companies that have relationships with each other. This paper utilizes an activity theory framework (Kim et al. 2002) to put forward the concept that members of the EIP conduct specific tasks that are assigned through a division of labor. The paper proposes a model wherein, through three types of learning processes (i.e., learning-by-investment, learning-by-doing, and learning-from-others), members obtain the optimal amount of specialized knowledge that is related to their own tasks. The three learning processes are modeled to have different impacts on depth and breadth of knowledge. Each member of an EIP decides optimal investments and efforts in each learning process, taking account of various conditions such as discount rate of cost, internal rate of return, and decay rate of knowledge. This study analyzes a mathematical model to explain the impact of each condition on individual decisions on the investment in three learning processes (Rao et al. 1995).

This research is expected to contribute to the literature in two ways. First, solutions of the mathematical model identify possible optimal individual decisions for obtaining the maximum benefit of knowledge under the given environmental conditions. The optimal solutions drawn from the analysis may explicitly provide individuals with proper guidance to knowledge investment. Second, the proposed model refers to an individual. However, the conclusions are equally applicable to a firm or a group as knowledge is a non-rival good in that there is no loss in sharing (Foray 2004). The findings of this study suggest appropriate guidelines for an EIP design policy to facilitate knowledge activities of members and achieve organizational effectiveness.