Yulong Gu1

1 Department of Computer Science, University of Auckland, New Zealand

ygu029@cs.auckland.ac.nz

Abstract. Organizational research on Knowledge Management (KM) has stud-

ied the nature of knowledge, the scope of KM, the factors and mechanisms that

affect KM outcomes, as well as the theoretical KM frameworks. This paper dis-

cusses the implications of past KM studies and identifies nine significant con-

textual, cultural, structural, managerial, cognitive and technological factors that

may impact the overall KM outcomes from a KM initiative or project.

1 Introduction

Knowledge is a mix of framed experience, values, contextual information and expert insight [1]. Polanyi classified human knowledge into “explicit” and “tacit” knowledge [2, 3]. The different views of knowledge lead to different perceptions of Knowledge Management (KM) [4]. For example, if knowledge is viewed as an object, or is equated with information access, then KM should focus on building and managing knowledge stocks. If knowledge is a process, then KM focus is on knowledge flow and the processes of knowledge creation, sharing, and distribution. A third view of knowledge is an organizational capability, then KM centers on building core compe-tencies, understanding the strategic advantage of know-how, and creating intellectual capital [5]. This paper establishes part of the theory base for an ongoing PhD project in the Department of Computer Science at the University of Auckland. The title of this project is Human Genetic Variation Knowledge Management. This paper reviews general KM issues that have been addressed by past organizational literature.

2 KM approaches in practice

Past KM practices have taken mainly two approaches or strategies: product-centric KM and process-centric KM, reflecting first and second KM focuses. But there is no successful experience reported as capability-centric approach (type three KM) – to build core competencies and to create intellectual capital [5].

Product-centric KM views knowledge as an objective asset to be codified, stored and managed like other organizational assets [6, 7]. It relies on the transformation of implicit or explicit knowledge from employees’ heads into written information in documents and the subsequent management of these documents [8, 9]. On the other hand, Process-centric KM views knowledge as residing with a person and/or a busi-ness process. There is no attempt in this strategy to formally capture and store knowl-edge; instead, it provides pointers to individuals who are likely to have the relevant expertise [6]. Process-centric KM applications include database of experts, decision aids and expert system, workflow management system, groupware, systems support-ing Community of Practice, and ‘hardwiring’ of social networks [5, 10].

3 Theoretical KM frameworks

KM models are proposed to guide KM studies and to provide best practice, e.g. the theoretical framework for organizing research on KM [11], KM effectiveness model [12], Knowledge Management Systems (KMS) Success Model [13], etc.

The contextual properties of units (e.g., individual, group, and organization), rela-tionships between units, and the knowledge itself may all affect KM outcomes (crea-tion, retention, and transfer) [11]. Furthermore, three key causal mechanisms (ability, motivation - rewards and incentives, opportunity) may help explain how and why certain contextual properties affect KM outcomes [11]. On the other hand, two organ-izational capabilities are referred as the “preconditions” for effective KM: (i) the knowledge infrastructure capability (social capital or network of relationship) and (ii) the knowledge process capability (knowledge integration), with the latter being influ-enced by contingent knowledge tasks, see Fig. 1.

Fig. 1. KM Effectiveness Model [12]

KMS are a class of Information Systems (IS) to manage organizational knowledge and to support knowledge processes [5]. KMS success model was developed from ISSuccess Model [14] and regarded the organizational effectiveness as KM outcome [13].Fig. 2 depicts how the individual’s and organization’s performance at workplace are improved by using quality KMS.

Fig. 2. KMS Success Model [13]

According to above figure, the quality of a KMS contributes to KM outcomes signifi-cantly; and it has at least three dimensions: (i) the technological resources – the abil-ity to develop, operate, and maintain a KMS, (ii) KMS form – the extent to which organizational memory and KM processes are computerized and integrated, and (iii) KMS level – the ability to bring past information to bear upon current activities [13].

4 Discussion and Conclusion

From past KM approaches and frameworks, we identify nine categories of contextual, cultural, structural, managerial, cognitive and technological issues that are critical for the KM efforts in an organization as: I. KM context [11], II. KM process [11], III. knowledge process capability [12], IV. contingent task characteristics [12], V. tech-nology/system quality [13], VI. knowledge/information quality [13], VII. perceived benefits and use/user satisfaction [13], VIII. knowledge infrastructure capability [12] and IX. KM outcome [12, 13]. Further exploratory and empirical studies will offer more insights on the significance of the nine constructs in an organizational KM pro-ject and the relationships among these issues during the project implementation. In the case of our project, by applying these nine constructs, we are trying to identify the context of human genetic variation knowledge management studies, the predisposi-tions and factors that may impact KM outcomes, and important KM processes in the genetics domain. We are also trying to understand the significance of the relation-ships among these issues. By taking an IS approach, our project will eventually pointa way for improved capture and dissemination of human genetic variation knowledge from routine genetic research activities to contribute to the global genetics know-ledgebase.

Acknowledgement

I’m in debt to many people for their contribution and inspiration to this ongoing pro-ject, especially to my two mighty supervisors: Prof. James Warren and Dr. Alexei Drummond. Thanks very much for your coolest SUPER VISIONS!

References

1.Davenport, T.H., Prusak, L.: Working Knowledge: How Organizations Manage What They

Know, Cambridge, MA: Harvard Business School Press, 1997.

2.Nonaka, I.: A dynamic theory of organizational knowledge creation. Organization Science,

5, 1, (1994) 14-37.

3.Polanyi, M.: The Tacit Dimension, London: Routledge & Kegan Paul, 1966.

4.Carlsson, S.A., El Sawy, O.A., Eriksson, I., Raven, A.: Gaining competitive advantage

through shared knowledge creation: in search of a new design theory for strategic informa-tion systems. In Proceedings of the Fourth European Conference on Information Sys-tems, Lisbon, (1996), pp. 1067-1075.

5.Alavi, M., Leidner, D.E.: Review: knowledge management and knowledge management

systems: conceptual foundations and research issues. MIS Quarterly, 25, 1 (2001), 107-136.

6.Dennis, A.R., Vessey, I.: Three knowledge management strategies: knowledge hierarchies,

knowledge markets and knowledge communities. MIS Quarterly Executive, 4, 4, (2005). 7.Schultze, U., Leidner, D.E.: Studying knowledge management in information systems

research: discourses and theoretical assumptions. MIS Quarterly, 26, 3, (2002), 213-242. 8.Bossen, C., Dalsgaard, P.: Conceptualization and appropriation: the evolving use of col-

laborative knowledge management. In Proceedings of the 4th decennial conference on Critical computing: between sense and sensibility CC '05, (August 2005), pp. 99-108.

9.Sambamurthy, V., Bharadwaj, A., Grover, V.: Shaping agility through digital options:

reconceptualizing the role of information technology in firms. MIS Quarterly, 27, 2, (2003), 237-263.

10.Brown, J.S., Duguid, P.: Organizing knowledge. California Management Review. 40, 3,

(1998), 90-111.

11.Argote, L., McEvily, B., Reagans, R.: Managing knowledge in organizations: An integra-

tive framework and review of emerging themes. Management Sci. 49(4 Special Issue), (2003), 571–582.

12.Lindsey, K.: “Measuring Knowledge Management Effectiveness: A Task-Contingent Or-

ganizational Capabilities Perspective,” Eighth Americas Conference on Information Sys-tems, pp. 2085-2090, 2002.

13.Jennex, M.E., Olfman, L.: A knowledge management success model: an extension of

DeLone and McLean’s IS success model. In Proceedings of Ninth Americas Conference on Information Systems, (August 2003).

14.DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent

variable. Information Systems Research, 3, 1, (1992), 60-95.下载本文