Knowledge transfer within information systems development teams: Examining the role of knowledge source attributes

Abstract

Knowledge transfer is considered to be an important topic for both researchers and practitioners. However, very little research has been pursued to understand the factors affecting knowledge transfer within teams, an important social unit within organizations. This study attempts to fill this void by examining factors that affect knowledge transfer within information systems development (ISD) teams. Taking a “connectionistic” epistemological perspective and drawing on the communications-based research on knowledge transfer, the theoretical model of the study proposes that the source's capability, credibility, and extent of communication will play an important role in determining the extent of knowledge transferred to recipients. Results from an empirical study involving student teams engaged in semester-long ISD projects supported the role of credibility and extent of communication. Interestingly, capability was not found to play a significant role in knowledge transfer. Possible explanations for this lack of significant effect of capability on knowledge transfer are presented.

Introduction

In order to gain and sustain a competitive advantage in the global economy, today's organizations need to effectively mobilize their knowledge resources [26], [35]. Knowledge transfer (KT) activity is central to the organizations' knowledge mobilization efforts [1], [28], [44]. Researchers argue that in today's “knowledge-driven organizations,” managers are often confronted with the question of “how can I encourage people to share what they know,” making knowledge transfer an important topic of interest for both academics and practitioners [46]. Knowledge transfer occurs when knowledge is diffused from one entity (e.g., an individual, group, or organization) to other entities. Knowledge may be purposefully transferred, or it may occur as an unintended outcome of other activities.

Research in the area of knowledge transfer has been conducted in many settings and for various objectives. Initial work on knowledge transfer focused on international technology transfers in light of Vernon's product life cycle [21], [73]. Research on knowledge transfer has also focused on inter-firm governance modes such as transfers among strategic alliances and between merged and acquired units [16], [68]. In addition, some research efforts have recently been directed toward understanding knowledge sharing within co-located and distributed teams [38], [42], [62], [64]. This work on teams is particularly important, given the acknowledgement that teams are a fundamental social unit within which team members share knowledge and learn from each other in the process of completing an assigned task [12], [66]. In spite of this recent interest in knowledge transfer, Levin et al. [46] assert that “there exists little systematic evidence” on what actually “promotes effectiveness of knowledge transfer.” They further argue that without knowing the critical linkages between factors associated with effective knowledge transfer, managers may be “left in the dark as to what they can do to foster valuable knowledge exchanges.” In response to these arguments by Levin et al. [46], the observations of other researchers [3], [79] that there have been limited studies on knowledge transfer within teams, and also to make our work relevant to the IS discipline, we examine factors affecting knowledge transfer within information systems development teams.

Information systems development (ISD) teams are deployed to conceptualize, design, develop and implement information systems to support business functions. Curtis, Krasner, and Iscoe [22] describe an ISD project as a process involving both communication and learning. A typical ISD team consists of individuals in various roles — designers, system developers, engineers, and project managers, to name just a few. To successfully build a large and complex system, team members have to continuously communicate and learn from each other regarding different issues ranging from the capabilities of the new system, application-specific algorithms, and architecture of the computers to articulating the intentions of the customers as reflected in the requirements statements [22]. Further, from their study of numerous large ISD projects involving multiple organizations, Curtis et al. [22] concluded that team members who possessed knowledge and were viewed as “exceptional designers” had the ability to control the direction of projects and positively influence their outcomes by making their knowledge available to other team members. This further lends support for the importance of knowledge sharing and transfer within ISD teams. Our study focuses on this very issue, which has not received much attention in the literature in the past.

Boisot [15] argues that successful knowledge transfer needs a “degree of resonance” between two or more agents, suggesting that knowledge transfer requires both the transfer (i.e., sending) of knowledge from the source agent, and the internalization/learning of that knowledge by the recipient agent. This transfer of knowledge depends not only on the type and complexity of the knowledge but also on the attributes and behaviors of the human agent sharing that knowledge [15]. Based on this, the specific research question examined in this study is: What are some of the key factors associated with ISD team members (i.e., the source agents) who are able to transfer significant knowledge to other team members (i.e., the recipient agents)?

Our research question approaches the issue of knowledge transfer with consideration of both the source and the recipient perceptions, which we believe is in itself a contribution to the literature on this topic.

The rest of the paper proceeds as follows. First, the theoretical background for this research is discussed. Next, a theoretical model for this study is posited, and the primary hypotheses of the study are presented. This is followed by a discussion of our research methodology, data collection procedures, instrument validation, analysis techniques, and the results. Finally, we discuss the study's findings, its implications for research and practice, its limitations, and future research directions.