Salvatore Belardo, Donald P. Ballou and Harold L. Pazer
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Introduction
From the beginning of the computer era systems analysts have been bedevilled by the difficulty of producing systems that satisfactorily meet user needs. All too often systems developed at considerable time and expense are not used, primarily because the result does not meet the user’s expectations and requirements. It is clear that one of the most significant factors in this failure is ineffective communication between the user group and the development group (Holtzblatt and Beyer, 1995). Accordingly, any action that enhances the effectiveness of the knowledge transfer process between user and developer would be a major contribution to developing systems deemed to be successful. Traditionally the development team has strong IS skills but is not necessarily familiar with the knowledge domain of the client. Conversely, the user group may have superficial knowledge of IS, but this is not sufficient to judge which development options would meet their needs. In general, the development and user groups have different mental frameworks, and so a statement that is perfectly understandable and meaningful to one party is either not absorbed by the other, or worse, is misinterpreted. Essentially, the quality of the knowledge transfer between the user and development groups is deficient.
This chapter examines the knowledge transfer problem between developers and users from the perspective of the effectiveness or quality of the information transfer. We focus on knowledge transfer during the systems analysis stage of the systems development life cycle. Effective knowledge transfer between the client and developer during the systems analysis phase is critical (Montezemi, 1988). If it is not done well, then there can be little hope that the project can be completed successfully (Marakas and Elam, 1998). There are of course many reasons why projects can fail, but it is necessary that the requirements that come out of the systems analysis phase be correct. Ensuring effective knowledge transfer during this phase is the purpose of this chapter.
It is important to note here that we are not proposing another systems development methodology. There are already many excellent methodologies found in the literature and practice including STRADIS, SSADM, IE, ETHICS, and UML to name a few. Avison and Fitzgerald (1995) and Tudor and Tudor (1995) as well as others have classified these methodologies according to frameworks that highlight history and themes of each and the issues both hard and soft that are encountered when the various methodologies are used. While each development methodology must begin with an analysis-like first stage, the success of this stage and ultimately the success of the final system depends to a large extent on the knowledge that each group (end users and system developers) has of the other’s domain and their ability to learn quickly. While knowledge and learning are implicit in several of the methodologies cited above, none of them have focused on both knowledge and learning as a means of assessing the potential for successful systems analysis.
We introduce concepts and techniques that facilitate tracking the effectiveness of knowledge transfer between the client and developer throughout the systems analysis phase. With these it is possible to identify the need for any midcourse corrections. Strategies are described for handling effective knowledge transfer in the context of several disparate environments. For this we first examine the nature of knowledge in the developmental context and then introduce a set of ideas and concepts that facilitate understanding knowledge transfer problems between developer and user. The primary reason we wish to be able to evaluate the level of knowledge transfer between the developer and user is to provide a framework that facilitates making team assignments that enhance the likelihood of a successful systems analysis phase.
Clearly an ideal or optimal knowledge transfer strategy varies substantially depending upon the characteristics of both the development team and the user group. What is not so obvious is how to encompass such situations into a general framework that can provide guidance as to what knowledge transfer strategy is most appropriate across a wide variety of characteristics possessed by the development and user teams as well as diverse project environments.
Knowledge, like the related term information, has many definitions. Sveiby (1997) found a number of definitions in the literature where the term knowledge was described in terms of awareness, sapience, practical ability, wisdom, certainty, and so forth. Davenport and Prusak (2000) describe knowledge as a fluid mix of framed experiences, values, contextual information, and expert insight that provides a framework for evaluation and incorporating new experiences and information. Alavi and Leidner (2001) describe knowledge as information possessed in the mind of individuals. Nonaka (1994) and Huber (1991) contend
Analysis and design of information systems: a knowledge quality perspective 45 that knowledge is a justified belief that increases an individual’s capacity to take effective action. One useful way to understand the term is to think of it in terms of unique categories. Many scholars have distinguished between two types of knowledge, tacit and explicit. Lubit (2001) describes explicit knowledge as the type that is conscious and can be put into words. Tacit knowledge on the other hand is that which develops when unconscious inductive mental processes create a representation of the structure of the environment showing the relationship between important variables. Lubit (2001) notes that people can have unconscious abstractions with which they can learn about the underlying complex structure of systems without being conscious of doing so or being able to articulate their understanding.
An underlying premise of our work is that the degree of conformity between the developers’ and users’ tacit knowledge for the other’s domain has a substantial and critical impact on the nature and effectiveness of the explicit knowledge transfer. This statement is implicitly supported by an extensive and longstanding body of research (e.g., Boland, 1978; Curtis et al., 1988; Vandenbosch and Higgins, 1996; Dorsey and Koletzke, 1997). The issue is framed succinctly in the work of Denzau and North (1994), who state, ‘Individuals with common cultural backgrounds and experiences will share reasonably convergent mental models, … and individuals with different learning experiences (both cultural and environmental) will have different theories (models, ideologies) to interpret that environment… [Having] similar models enables [individuals] to better communicate and share their learning.’ For our work it is necessary to fix the concepts of tacit and explicit knowledge.By tacit knowledge we mean the mental model used by an individual to process information produced by others or to absorb observations (Polanyi, 1966). Thus individuals may very well interpret or react differently to the same stimuli depending on their mental model. Explicit knowledge is a framework or structure that transforms data communicated between parties into information (see, for example, Lyles and Schwenk, 1992). Depending upon one’s mental model, the explicit knowledge provided by one party could be interpreted by another as intended or could be badly misinterpreted, as determined by the receiving party’s tacit knowledge. Thus the level of tacit knowledge each side has of the other’s domain affects the correctness and rapidity of the explicit knowledge transfer.
IS professionals use the terms data, information, and knowledge on a routine basis with the full realization that they do indeed represent different concepts (Huang et al., 1999).Our use of knowledge is in the context of Bloom’s taxonomy (Bloom, 1956), to be examined later, which in essence provides a graded measure of one’s knowledge. Essentially it is a scale that can be used to evaluate the level of explicit knowledge possessed by an individual. As such it can be equated with explicit
knowledge. Explicit knowledge, in turn, can be thought of as providing a structure that transforms data into information.
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Knowledge transfer environments
It is reasonable to expect that the knowledge transfer approaches appropriate for each combination of the developer’s tacit knowledge of the user’s domain and the user’s tacit knowledge of the developer’s domain differ significantly. As the project proceeds, the developer’s knowledge of the user’s domain grows, and conversely. However, knowledge on the part of the user of the developer’s domain, although certainly helpful, is not critical for a successful systems analysis phase. For this reason we do not explicitly model growth in the user’s knowledge of the development team’s area of expertise. Not modelling this has the additional benefit of simplifying the presentation of the concepts and our methodology. As a result of this modelling decision, the development team has the responsibility to close the knowledge transfer gap. Accordingly it needs to be constituted so as to accomplish this within the timeframe allotted for analysis.
To facilitate understanding of how to optimize knowledge transfer for all situations, we use as our foundation Bloom’s taxonomy (Bloom, 1956), which serves as a benchmark for evaluating the levels of explicit knowledge transfer. To aid in measuring and tracking the developer’s knowledge of the user’s domain, we cross-dimension the Bloom categories with several descriptors or dimensions of knowledge. This gives IS managers who are responsible for building a development team a tool for assessing initially the potential for effective knowledge transfer for a particular choice of individuals, given that development team formation must be done in the context of the environment.