Questioning the role of IT in the success of KM Systems

Transcription

1 Questioning the role of IT in the success of KM Systems Research Guide: Dr. Don Turnbull Assistant Professor School of Information Amit Sharma Knowledge Management Systems INF 385q School of Information UT - Austin

2 Abstract This paper reviews the role of Information Technology (IT) on various success models used for implementing Knowledge Management Systems (KMS). The paper initially discusses the brief history of KMS and offers some insights about various KMS Success factors. There are 4 success models taken into consideration and I would analyze the two best KMS success models based on various KMS success factors. The two best successful models are then analyzed for the role of IT in their success. The paper is being summarized with the help of diagram which provides insight about the role of IT in KM Systems. What is KMS? Knowledge Management Systems (KMS) are technologies that support Knowledge Management (KM) in organizations including knowledge generation, codification and transfer [1]. This concept provided by Ruggers in 1997 helps in identifying some of the functions of KM System and has further provided basis for defining similar systems like Collaborative Systems, Information Systems and KMS. A further insight to the concept of Information Systems was provided by Alter [2] who described Information Systems as humans or machines limited to processing information by performing six types of operations: capturing, transmitting, storing, retrieving, manipulating, and displaying. These descriptions did not provide what a KMS is until 2001, when Alavi and Leidner [3] defined KMS as IT-based systems developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer, and application. They observed that not all KM initiatives will implement an IT solution, but they support IT as 2

3 an enabler of KM. This definition tried to distinguish between the role of IT and KMS and the roles that each of them play while implementing any KM System. The definition of Alavi and Leinder also helped in defining the systems which were not completely dependent upon IT like KM Systems for its solution but rather many factors came into play while implementing those kinds of systems. Alavi and Leidner [3] also classified the KMS/KMS tools based on the Knowledge Life Cycle stage that they support. This model has four stages knowledge creation, storage/retrieval, transfer, and application and it was expected that the KMS will use technologies specific to supporting the stage for which the KMS was created to facilitate. This model further helps us in realizing the role of IT as a supporting tool. Maier [4] expanded on the IT concept for the KMS by calling it an ICT (information and communication technology) system that supported the functions - of knowledge creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formalization, visualization, distribution, retention, maintenance, refinement, evolution, accessing, search, and application. Stein and Zwass [5] define an Organizational Memory Information System (OMIS) as the processes and IT components necessary to capture, store, and apply knowledge created in the past on decisions currently being made. This definition again states the importance of IT in KM Systems. Jennex and Olfman [6] expanded this definition by incorporating the OMIS into the KMS, and adding strategy and service components to the KMS. There is no one definition that can define what a KMS is. However, the definition provided by Jennex and Olfman seems to be the most suitable for defining a KMS as it adds two new 3

4 components to OMIS. These components help in identifying the goals and the services that a KMS can provide to its users. The various definitions discussed above have provided us with a brief insight about the functionalities and components of KMS. An interesting fact about all the above definitions is that all of them define KMS as a part of a larger IT System. In our study about KMS success models, these definitions along with the KMS Success factors provided us with a basis for understanding the role of IT in a KMS. KMS SUCCESS FACTORS Jennex and Olfman [13] defined a successful KMS as the one that has the functions of knowledge creation, storage/retrieval, transfer, and application. However, I believe that the success of KMS mainly depends on factors which are present outside the system. Mandviwalla [7] summarized the state of the research and described several strategy issues affecting the design of a KMS. These include the focus of the KMS, the quantity of knowledge to be captured and in what formats, who filters what is captured, and what reliance and/or limitations are placed on the use of an individual s knowledge. Additional technical issues affecting KMS design include knowledge storage/repository considerations, how information and knowledge are organized so that the KMS can be searched and items can be linked to appropriate events and use, and processes for integrating the various repositories and for re-integrating information and knowledge extracted from specific events. Some management issues include how long the knowledge is useful, access locations (because users rarely access the KMS from a single location, which leads to networking and security concerns), and the work activities and 4

5 processes that utilize the KMS. This has made the management invest equally on the IT to support KMS. KMS is about sharing knowledge and in today s economy where globalization seems to be the mantra for success, the physical location of the resource should not affect in the knowledge sharing process of any system. IT in this regard helps in making people from different time zones and locations come together and make them contribute towards any KMS implemented in an organization. Ackerman [8] studied six organizations that had implemented his Answer Garden system. Answer Garden is a system designed to grow organizational memory in the context of help-desk situations. Ackerman and Mandel [9] found that narrow expectations play an important role and smaller task based systems were more effective at the sub-organization level. Only one organization had a successful implementation because expectations of the capabilities of the system exceeded the actual capabilities. They refer to this narrower system as memory in the small. Jennex and Olfman [10] studied three KM projects to identify design recommendations for building a successful KMS. These recommendations include: 1. Develop a good technical infrastructure by using a common network structure; adding KM skills to the technology support skill set; using high end PCs, integrated databases, and standardizing hardware and software across the organization. 2. Incorporate the KMS into everyday processes and IS by automating knowledge capture. Have an enterprise wide knowledge structure. 3. Have Senior Management support. 4. Allocate maintenance resources for KMS such as storage space for data backup. 5. Train employees on use and content of the KMS. 6. Create and implement a KM Strategy/Process for identifying/maintaining the knowledge base. 5

6 7. Expand system models/life cycles to include the KMS and assess system/process changes for impact on the KMS. 8. Design security into the KMS. 9. Build motivation and commitment by incorporating KMS usage into personnel evaluation processes, implementing KMS use/satisfaction metrics, and identifying organizational culture concerns that could inhibit KMS usage. In addition to this, Jennex and Olfman [11] performed a longitudinal study of KM on one of these organizations and found that new members of an organization did not use the KMS due to a lack of context for understanding the knowledge and the KMS itself. They found that these users needed pointers to knowledge more than codified knowledge. Jennex, Olfman, and Addo [12] also investigated the need for having an organizational KM strategy to ensure that knowledge benefits gained from projects are captured for use in the organization. They surveyed Year 2000 (Y2K) project leaders and found that benefits from Y2K projects were not being captured because the parent organizations did not have a KM strategy/process. Their conclusion was that KM Systems can exist in such kind of projects and assists the employees by providing them with the knowledge that they are seeking for. However, this small support does not guarantee that the KMS being used on the whole organization level can be successful as well. Davenport [14] studied 31 projects in 24 companies for understanding the factors affecting the success of KM Systems. Eighteen projects were determined to be successful, five were considered failures, and eight were too new to be rated. Eight factors were identified that were common in successful KM projects. These factors were: Senior management support. 6

7 Clearly communicated KMS purpose/goals. Linkages to economic performance. Multiple channels for knowledge transfer. Motivational incentives for KM users. A knowledge friendly culture. A solid technical and organizational infrastructure i.e. tools A standard, flexible knowledge structure. KNOWLEDGE MANAGEMENT SUCCESS MODELS 1. Knowledge Value Chain Success Model Bots and de Bruijn [15] studied KM Systems and determined that the best way to judge good KM was through a knowledge value chain. This process evaluates at each step for its effectiveness and the step was considered good only if all the designated activities were performed at each step and every step added to the competitiveness. The model was developed by viewing and contrasting KM through an analytical (technical) perspective and an actor (user) perspective. These perspectives are conflicting and KM assessment occurs by determining how well the KMS meets each perspective at each step. Not much emphasis was put on the IT side while developing this success model. It evaluates the system on taking both user as well as technical side equally and the entire success of the KM system was dependent on the level of competitiveness that each step added to the system. 7

8 Figure 1: KMS Value Chain Success Model [15] Page Massey, Montoya-Weiss, and Driscoll KM Success Model Massey, et al. [16] presented a KM success model derived from a case study of Nortel. The model is based on the framework proposed by Holsapple and Joshi [17] and reflects that KM success flows from understanding the organization, its knowledge users, and how they use knowledge. It recognizes that KM is an organizational change process and KM success cannot separate itself from organizational change success. The result is that KM success is essentially defined as improving organizational or process performance. The model is presented in Figure 2. Key components of the model are: 8

9 Figure 2: Massey, Montoya-Weiss, and Driscoll KM Success Model [16] Page 3. 1) KM Strategy which defines the processes using knowledge and what that knowledge is; the sources, users, and form of the knowledge; and the technology infrastructure for storing the knowledge. 2) Key Managerial Influences which defines management support through leadership, allocation and management of project resources, and oversight of the KMS through coordination and control of resources and the application of metrics for assessing KMS success. 9

10 3) Key Resource Influences these are the financial resources and knowledge sources needed to build the KMS. 4) Key Environmental Influences describe the external forces that drive the organization to exploit its knowledge to maintain its competitive position. This model tries to incorporate maximum number of success factors. Understanding People is an important stage which plays an important role in determining the success of a KM System and is missing in the Value Chain Model. However, this model fails to include the Culture of an Organization factor which according to me is the next most important factor after Understanding People/Users. 3. Lindsey KM Effectiveness Model: Lindsey [18] proposed a KM effectiveness model based on two parameters: Knowledge Infrastructure Capability Knowledge Process Capability. Knowledge infrastructure capability: represents social capital; the relationships between knowledge sources and users; and is made operational by technology (the network itself), structure (the relationship), and culture (the context in which the knowledge is created and used). Knowledge process capability: represents the integration of KM processes into the organization, and is instantiated by acquisition (the capturing of knowledge), conversion (making captured knowledge available), application (degree to which knowledge is 10

11 useful), and protection (security of the knowledge). Tasks are activities performed by organizational units and indicate the type and domain of the knowledge being used. Tasks ensure the right knowledge is being captured and used. KM success is measured as satisfaction with the KMS. Figure 3 illustrates the Lindsey model. The success model depends minimal on the Information Technology. Figure 3. Lindsey KM Effectiveness Model [18] 4. Jennex and Olfman Success Model Jennex and Olfman [19] presented a KMS Success model that is based on the DeLone and McLean [20], [21] IS Success Model. Figure 4 shows the KMS Success Model. This model evaluates improvement in organizational effectiveness based on use of and impacts from the KMS. The net impact of this model can be realized as success of both 11

12 organizational and individual impact. This model recognizes that the use of Knowledge/Organizational Memory (OM) may have good or bad benefits and allows for feedback from these benefits to drive the organization to either use more KM Systems/OM or to forget specific Knowledge/OM. Jennex and Olfman state that the use of KM System helps in improving the performance of an individual in an organization and in turn leads to the overall success of any organization. Since overall Organization success is not a summation of individual success therefore it is very difficult to draw a direct relationship between individual and organizational impacts on the KM System. Figur 4. Jennex and Olfman Success Model [19] Page 8. 12

13 The model depicts the various factors that influence the effectiveness of a Knowledge Management System and Technological Resources remains one of them. Analyzing the various Models: The aim here is to analyze the relationship between various success models and the role of Information Technology in the two best success models. All the four models discussed above are based on some theoretical foundation. The Value Chain Model is based upon Value Chain approach. The Massey et al. model is based on the Hosapple and Joshi framework. The Lindsey model utilizes Organizational Capability Perspective Theory and Contingency Perspective Model. The Jennex and Olfman model utilizes the widely accepted DeLone and McLean IS Success model. According to Jennex and Olfman [13], depending upon the ability to generalize form the theory, the two models that are widely accepted are Jennex and Olfman Model and Value Chain Model. The Value Chain model is suitable for organizations that would like to identify strategic processes whereas the Lindsey model takes into account the task specific components which make it difficult to assess the overall effectiveness of a KMS. However comparing all the 4 models, the two best approach for developing a successful KM System can be taken as Jennex and Olfman model and Value Chain Model which try to incorporate maximum recommendations/factors [Page 5]. This can be summarized with the help of table given below: 13

14 Recommendation Value Chain Model Jennex and Olfman Model Integrated Technical Infrastructure including networks, computers, software share knowledge stage Technical Resources Stage databases/repositories,, KMS experts. A Knowledge Strategy that identifies users, sources, processes, storage Strategy stage strategy, knowledge and links to KM Strategy /Process Stage knowledge for the KMS. A common enterprise wide knowledge structure that is clearly articulated and No clear tie Form Stage easily understood. Motivation and Commitment of users including incentives and training. Apply knowledge stage Perceived Benefit Stage An organizational culture that supports learning and the sharing and use of No clear tie Perceived Benefit Stage knowledge. Senior Management support including allocation of resources, leadership, and Implied no clear tie Perceived Benefit Stage providing training. Measures are established to assess the impacts of the KMS and the use of Return stage Net Impacts Stage knowledge as well as verifying that right knowledge is being captured. Security/protection of knowledge. No clear tie No clear tie Comparing the role of IT in Value Chain and Jennex and Olfman Success Model: The Value chain model was analyzed based on the analytical (technical) and actor (user) perspective. The values that dominate the analytical perspective can be summed up by the knowledge management mission paraphrased by Davenport [22] as getting the right knowledge with the right people at the right time. The analytical perspective of knowledge management is good only when it addresses all the needs of an organization using the best tools available. However, in my view, a KMS can be termed successful and adds to the organizational success only when individuals share knowledge among themselves. The Entry rule in the above model identifies the involvement of professionals in a KMS. A similar study in Jennex and Olfman model helps identifying the user/perceived benefits as one of the important factors for success of KMS. Both the 14

15 models have defined the role of IT as a tool used for transferring and encoding of knowledge. The main emphasis is set towards the user involvement rather than technology. The case studies in both the models help in analyzing the fact that organizations have lost millions when the users have failed to contribute towards the KMS. This fact has been further reiterated with the help of case studies done by Davenport [23] in order to define the importance of users in a KMS. There is quite a lot of research that is relevant towards the user s contribution to any system. Theorists as disparate as Dewey and Piaget have consistently shown that the mere presentation of information does not necessarily result in learning. People have to become actively involved for behavior to change, for insight to occur and for problems to be solved. It can be stressed upon that this learning and insight has a significant social component, even if the resulting knowledge was of a type we might classify as mathematical or scientific. Yet, all too often, large organizations come to believe that simply making more information available more widely will solve knowledge management problems. Motivation has been established as one of the major sources of failure in adoption of groupware in general. In Orlikowski's [24] study of the failure to adopt Lotus Notes in a consultancy, the failure was attributed to the fact that individuals were compensated according to their competitive talents. There was no incentive to share one's best ideas if they were then going to be seen as common, no longer unique. In other organizations, where incentives are aligned with how much others use the knowledge you make 15

16 available to them, Notes and other jointly authored groupware systems succeeded. With the help of these case studies we scan analyze that knowledge is bound up with human intelligence, shaped by social assumptions, and requires active engagement on the part of the users/professionals. The KMS Success models have supported the importance of the User participation in KMS by including: 1) Actor perspective in Value Chain Model [15]: which emphasizes on the fact that KM is good when it achieves involvement of professionals, an open culture of free exchange of ideas, and 2) Intend to Use/Perceived Benefits dimension in Jennex and Olfman Model [20]: which is a construct that measures perceptions of the benefits of the KMS by users. It is good for predicting continued KMS use when use of the KMS is voluntary, and amount and/or effectiveness of KMS use depends on meeting current and future user needs. Thus by analyzing the above models, we can conclude that IT acts as a tool for achieving success in KMS. In my view, the process of achieving a successful KMS can be summarized with the help of following diagram: Tacit Knowledge + Explicit Knowledge > Explicit Knowledge IT (Catalyst) (Before KMS implementation) (After successful implementation) 16

17 The above diagram shows that an organization possesses either tacit or explicit knowledge (left hand side of the diagram). According to Nonaka [25], tacit knowledge is present in a knower s mind and explicit knowledge is information which can be codified and transferred with the help of storage/repositories and retrieval tools. An organization can gain benefits from KMS only if it is able to convert all the knowledge present within an organization (both explicit and tacit) into explicit knowledge. We perceive IT as a Catalyst in the above process as it helps in facilitating the rate with which the explicit knowledge can be transferred and codified in an organization. The principal entities that are participating in the above process are the users/professionals in an organization. It is the knowledge of the user which a KMS tries to capture, transfer and codify with the help of IT. Thus, the above diagram summarizes my analysis about the role of IT in a KMS. The various definitions of KMS discussed earlier in the paper describe KMS as part of Information Systems where IT plays an important role in defining those systems. After analyzing the KMS success models and adding my own insight [Page 16], we can come to a conclusion that success in a KMS is more than IT dependent and involves People and Organization. In future we can expect new definitions of KMS surfacing as researchers and theorists discover new dimensions affecting the success of KMS. References: [1] Ruggles, R. (1997). Knowledge Tools: Using Technology to Manage Knowledge Better. Working paper, [2] Alter, S. (1999).A general, yet useful theory of information systems. Communications of the Association for Information Systems,1(13). 17

18 [3] Alavi, M. & Leidner, D.E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly,25(1), [4] Maier, R. (2002). Knowledge management systems: Information and communication technologies for knowledge management. Berlin: Springer-Verlag. [5] Stein, E.W. & Zwass, V. (1995). Actualizing organizational memory with information systems. Information Systems Research, [6] Jennex, M.E. & Olfman, L. (2004).Modeling knowledge management success. Proceedings of the Conference on Information Science and Technology Management (CISTM). [7] Mandviwalla, M., S. Eulgem, C. Mould, and S. V. Rao, Organizational Memory Systems Design, Unpublished Working Paper for the Task Force on Organizational Memory, F. Burstein, G. Huber, M. Mandviwalla, J.Morrison, and L. Olfman, (eds.),31st Annual Hawaii International Conference on System Sciences [8] Ackerman, M., "Definitional and Contextual Issues in Organizational and Group Memories" Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, pp , [9] Ackerman, M. and E. Mandel, "Memory In the Small: An Application to Provide Task-Based Organizational Memory for a Scientific Community", 29 th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press, pp , [10] Jennex, M.E. and L. Olfman, Development Recommendations for Knowledge Management/Organizational Memory Systems Information Systems Development Conference [11] Jennex, M.E. and L. Olfman, Organizational Memory/Knowledge Effects on Productivity, A Longitudinal Study, 35th Hawaii International Conference on System Sciences, IEEE Computer Society, January [12] Jennex, M.E., L. Olfman, and T.B.A. Addo, The Need for an Organizational Knowledge Management Strategy, 36th Hawaii International Conference on System Sciences, IEEE Computer Society, January [13] Jennex M. and Olfman L. Assessing Knowledge Management Success/Effective Models, 37th Hawaii International Conference on System Sciences [14] Davenport, T.H., D.W. DeLong, and M.C. Beers, Successful Knowledge Management Projects, Sloan Management Review, Volume 39, Number 2,

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