Theories and Definitions

Knowledge transfer is becoming increasingly important in organizations. Organizations that are able to transfer knowledge effectively from one unit to another are more productive and more likely to survive than those that are less adept at knowledge transfer. (Argote et. al., 2000).Dixon (2000) identifies five different types of knowledge transfer situations for procedural knowledge: serial transfer, near transfer, far transfer, strategic transfer, and expert transfer. Knowledge can be viewed through the following perspectives (Alavi and Leidner, 2001):

1)      knowledge vs. data vs. information,

2)      knowledge as a state of mind,

3)      knowledge as objects that can be stored in repositories and manipulated,

4)      knowledge as a process of simultaneously knowing and acting,

5)      knowledgeas capability or resource with the potential of improving organizational performance.

This section surveys the literature associated with knowledge/information sharing highlighting two sharing mechanisms illustrated in the Overview section.

Basic Concepts (data, information, and knowledge)

 “Knowledge is neither data nor information, though it is related to both, and the differences between these terms are often a matter of degree…Confusion about what data, information, and knowledge are – how they differ, what those words mean – has resulted in enormous expenditures on technology initiatives that rarely deliver what the firms spending the money needed or thought they were getting” (Davenport and Prusak, 1998, pp.1).

Two views are presented considering the relationships among data, information, and knowledge:

l        Knowledge derives from information as information derives from data (Davenport and Prusak, 1998, pp.6): data as a prerequisite for information; information as a prerequisite for knowledge

l        Data emerges only after we already have information, and information emerges only after we already have knowledge (Tuomi, 1999): knowledge as a prerequisite for information; information as a prerequisite for data

Definition (knowledge/information sharing)

Knowledge sharing is viewed as “the set of processes (both cognitive and behavioral) that take place when a group of people create and shape new knowledge together” (El Sawy et. al., 2001, pp. 152). Such view considers knowledge sharing inclusively as sharing of existing knowledge and new knowledge creation through simultaneous sharing existing knowledge. However, there is no inclusive definition of information sharing even though there is a growing recognition of the importance of information sharing on changing our society. Tendency towards information sharing is “part of attitudes toward prosocial organizational behaviors” (Jarvenpaa and Staples, 2001, pp. 156). The way information is framed will affect its shared meanings (Tversky and Kahneman, 1985).

Theoretical exploration

Knowledge/information as output

Organization behavior

A dynamic theory of organizational knowledge creation

Nonaka(1994) proposes a paradigm for managing the dynamic aspects of organizational knowledge creating processes. The author identifies two dimensions of knowledge: tacit knowledge (“rooted in action, commitment, and involvement in a specific context”, pp.16) and explicit knowledge (“transmitted in formal, systematic language”, pp.16). It is proposed that organizational knowledge is created through a continuous dialogue between tacit and explicit knowledge. The author further identifies four patterns of interaction involving tacit and explicit knowledge (illustrated in the following figure). In addition, he suggests that organizational knowledge creation occurs when all four modes of knowledge creation form a continual cycle which is shaped by a series of shifts between different modes of knowledge creation. As such, the sharing mechanism involved sheds light on the transfer between four modes. It is more likely that such perspective stresses the view of knowledge as processes.

Kusunoki,Nonaka, and Nagata (1998) presents a conceptual framework assuming that organizational capabilities consist of multilayered knowledge. The central idea is that the knowledge that shapes organizational capabilities has a multilayered structure (illustrated in the following figure) rather than a singly entity. It further confirms that the knowledge/information in this line is viewed as processes rather than properties.

Organizational learning

The organizational learning perspective on the knowledge transfer process suggests that knowledge transfer for innovation will benefit from an openness that encourages and allows multiple perspectives on problems (Boland, Tenkasi, and Te’eni, 1994). According to Weick (1995), organizational learning directs our attention to that different people may have different views on the reality of the same raw facts. Senge (1990) suggests five types of behaviors with such notion in the organizational learning process: system thinking, clarifying personal visions, shifting mental models, building a shared vision, and engaging the team in joint and open dialogue. Goodman and Darr (1998) conceptualizes organizational learning as the process by which one unit acquires knowledge from another unit in the same organization. Another argument is that an organization learns through its processing of information where the range of its potential behaviors is changed (Huber, 1991).

Communication

Transactive memory theory

An analysis of how group members could share task knowledge with one another can be found in Wegner’s work on transactive memory, a shared system for encoding, storing, and retrieving information (Wegner, 1987, 1995; Wegner, Gluliano, and Hertel, 1985). Transactive memory theory is useful for predicting how organizational members acquire, store and retrieve knowledge (Hollingshead, Fulk, Monge, 2001). Members can learn informally who is the relative expert across knowledge domains through shared experiences and conversations with one another (Hollingshead, 1998; Wegner, 1987). Key processes of a transactive memory system include: Directory updating, whereby people learn what others are likely to know; Information allocation, where new information is communicated to the person whose expertise will facilitate its storage; Retrieval coordination, which is a plan for retrieving needed information on any topic based on knowledge of the relative expertise of the individuals in the memory system (Wegner, 1995).

Knowledge/information as property

IS Research

Resource-based view

The resource-based view of the firm rose in response to Porter’s (1980) structural perspective of strategy. The resource-based view of knowledge capital as an organizational asset is an economic theory of knowledge transfer in the firm (Majchrzak, Neece, and Cooper, 2001). According to the resourced-based view, the firm’s resources and capabilities can be a source of “excess income” and the firm should have as many different organizational capabilities as possible to generate excess profits (Barney, 1991; Dierickx and Cool, 1989; Grant, 1996; Verona, 1999). This perspective on knowledge management suggests that a knowledge transfer process needs to ensure that individual knowledge bases are developed, rather than creating a sing centralized knowledge base. Conner and Prahalad (1996) proposes a resource-based-knowledge-based theory to investigate the effects of cognitive costs of tacit knowledge transfer on the form of governance of the firms. Szulanski (2000) points out the limitation of such view that it ignores the cost to transfer knowledge and further suggests that, in addition to developing individualized and integrative knowledge base, knowledge transfer distinguishes by motivation, organizational context, and task need, given the stage in the knowledge transfer process. 

Szulanski (2000) offers a process model of knowledge transfer. The model identifies stages of transfer and factors that are expected to correlate with difficulty at different stages of the transfer. The general expectation is that factors that affect the opportunity to transfer are more likely to predict difficulty during the initiation phase, whereas factors that affect the execution of the transfer are more likely to predict difficulty during subsequent implementation phases. Measures of stickiness are developed for each stage of the transfer to explore the predictive power of different factors at different stages of the process.

Social exchange theory serves as a basis as to how the knowledge sharing with organizations outside a central relationship can benefit the central relationship. It has long been recognized that knowledge sharing is one crucial outcome of relationships across organizations and organizations form relationships to facilitate knowledge transfer or knowledge sharing (Zander and Kogut, 1995; Dyer and Singh, 1998). Thus, relationships between organizations may be a source of competitive advantage. Kim and Mauborgne (1998) and Culnan and Armstrong (1999) argue that the rules of social exchange govern knowledge sharing.

Extension of social exchange theory

The Constant et. al. (1994) theory of information sharing is built on social exchange theory (Jarvenpaa and Staples, 2001). Constant et. al. (1994) advances a theory of information sharing to understand the factors that foster or inhibit information sharing in technologically advanced organizations. The theory steps toward communication and information exchanges among “organizationally-remote strangers” (pp. 401) who will never physically meet with each other. Their theory accommodates both organizational contextual factors as well as psychological factors including feelings, values, and self-identities.

Integration of IS and organizational requirements

Lee and Leifer (1992) develops a framework for understanding the alignment between organization and information systems (IS) structure that is based on the linking concept of information sharing. The authors explain five dimensions of IS structure (hardware distribution, locus of application development, database location, planning decision authority, and systems boundary) used as descriptors for classifying IS structure into four modes: centralized, decentralized, hybrid, and interorganizational systems management. It is suggested that IS can be effective coordinating mechanisms, along with rules, hierarchy, planning, and teams.

Organization Research

Organizational memory theory

Organization researchers focusing on insights of information sharing use models and theories of organizational memory as starting points (Anand, Manz, and Glick, 1998). Organizational memory is defined as the information and knowledge known by the organization and the processes by which such information is acquired, stored, and retrieved by organization members (Anand, Skilton, and Keats, 1996; Huber, 1991; Walsh and Ungson, 1991; Weick, 1979).

Anand, Manz, and Glick (1998) identifies information management challenges using an extended model of group memory to organizations. In addition, they suggest that organizations can address these challenges by locating a large portion of their information-processing activities outside their formal boundaries, by adopting novel socialization tactics, and by focusing on the management of soft knowledge forms. Furthermore, they argue that information technology needs to be complemented by organization-level processes related to organizational memory.

Information processing theory

Information processing theory takes contingency perspective. It argues that organizations structure themselves to reduce the information processing load (Galbraith, 1973). In addition, the match between the information processing requirements and information processing capabilities may facilitate organizations to be more effective.

Communication

Public goods theory

The public goods theory of collective action (Marwell and Oliver, 1993; Olson, 1965; Samuelson, 1954) considers how best to trigger members of a collective to contribute their resources to the creation and/or maintenance of a collective resource accessible to all members of the “public”. Two features of public goods are nonexcludable and nonrival. Public goods theory is useful for predicting whom, how much, and when members will contribute and retrieve knowledge and suggests some insights that can foster knowledge sharing (Hollingshead, Fulk, Monge, 2001). These steps include training in system features and knowledge strategies; systems design with options for selective dyadic knowledge exchange (to reduce costs associated with sharing sensitive information); and mechanism development to increase commitment (by providing follow-on rewards for project competition that depend on effective knowledge sharing) (Hollingshead, Fulk, Monge, 2001).