The ambition level using knowledge management systems can be defined in terms of stages of knowledge management technology as illustrated again in Figure 5.4.
Stage I is labeled “end-user-tool systems” or “person-to-technology,” as information technology provides people with tools that improve personal efficiency. Examples are word processing, spreadsheets and presentation software. Stage II is labeled
“who-knows-what systems” or “person-to-person,” as people use information technology to find other knowledge workers. Examples are yellow-page systems, CVs and intranets. Stage III is labeled “what-they-know systems” or “person-to- information,” as information technology provides people with access to information that is typically stored in documents. Examples of documents are contracts, articles, drawings, blueprints, photographs, e-mails, presentations and reports (Kankanhalli et al., 2005). Stage IV is labeled “how-they-think systems” or “person-to-system,”
in which the system is intended to help solve a knowledge problem. Examples are expert systems and business intelligence.
In e-business, the electronic exchange of information requires that information is stored in electronic form. Therefore, we suggest the following proposition:
P7: Higher stages of knowledge management technology provide more effective support for electronic business.
In a different empirical setting, Ko et al. (2005) studied antecedents of knowledge transfer from consultants to clients in enterprise system implementations. They found that the greater the shared understanding between a consultant and a client, the greater the knowledge transfers. Furthermore, the greater the absorptive capacity of a client, the more intrinsically motivated the client and the consultant, the more credible the consultant, the greater a client’s communication decoding competence, the greater a consultant’s communication encoding competence and the greater the knowledge transfer.
The stages of growth model can be interpreted as alternative strategies, where the alternative strategies are person-to-tools strategy, person-to-person strategy, person- to-information strategy and person-to-system strategy. A comparison of these four alternatives can be made to the classification into personalization vs. codification strategy by Hansen et al. (1999). In this comparison, S tages I and II represent per- sonalization, while Stages III and IV represent codification.
Figure 5.4. Stages of growth model for knowledge management technology
Stage I
Person-to-technology End-user-tool systems.
Stage II
Person-to-person Who-knows-what systems.
Stage III
Person-to-information What-they-know systems.
Stage IV
Person-to-system How-they-think systems.
Level of IT-supported knowledge management in the organization
Time inyears
P8: The codification strategy for knowledge management systems is more effec- tive for e-business than the personalization strategy.
Stages of knowledge management technology is a relative concept concerned with IT’s ability to process information for knowledge work. IT at later stages is more useful to knowledge work than IT at earlier stages. The relative concept implies that IT is more directly involved in knowledge work at higher stages, and that IT is able to support more advanced knowledge work at higher stages.
Some benchmark variables for the stages of growth model for knowledge manage- ment technology are listed in Figure 5.5.
In knowledge management technology, the intelligence continuum is an interesting concept. The intelligence continuum is a collection of key tools, techniques and processes. Examples are data mining and business intelligence. Taken together, they represent a system for refining the data raw material stored in data marts and/or data warehouses and maximizing the value and utility of these data assets for any orga- nization. The first component at one end of the continuum is a generic information system, which generates data that is then captured in a data repository.
In order to maximize the value of the data and use it to improve processes, the techniques and tools of data mining, business intelligence and analytics must be applied to the data warehouse. Once applied, the results become part of the data set that are reintroduced into the system and combined with the other inputs of people, processes and technology to develop an improvement continuum. Thus, the intelligence continuum includes the generation of data, the analysis of these data to provide a diagnosis and the reintroduction into the cycle as a prescriptive solu- tion. In terms of the stages of growth model, a prescriptive solution from a system typically occurs at Stage IV.
An important application in the intelligence continuum is data mining, which occurs at Stage III. Due to the immense size of the data sets in most organizations, comput- erized techniques are essential to help knowledge workers understand relationships and associations between data elements. Data mining is closely associated with databases and shares some common ground with statistics since both strive toward discovering structure in data. However, while statistical analysis starts with some kind of hypothesis about relationships in data, data mining does not. Data mining deals with heterogeneous databases, data sets and data fields. Data mining, then, is the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns from data.
Another technology-driven technique like data mining connected to knowledge management is the area of business intelligence and the now newer term of business analytics. The business intelligence term has become synonymous with an umbrella
description for a wide range of decision-support tools, some of which target specific user audiences. At the bottom of the business intelligence hierarchy are extractions and formatting tools, which are also known as data-extraction tools. The next level is known as warehouses and marts.
Human intelligence tools form the final level in the hierarchy and involve human expertise, opinions and observations recorded to create a knowledge-based reposi- tory. These tools are the very top of business intelligence and represent business analytics specifically focused on analytic aspects. Here we find rule-based expert systems, fuzzy logic and system dynamics modeling.
System dynamics modeling is an analytical tool to study business dynamics. The modeling process starts with sketching a model, then writing equations and speci- fying numerical quantities. Numerical quantities can be the result of data mining.
Next, the model is simulated with simulation output automatically saved as a dataset.
Finally, the simulation data can be examined with analysis tools to discover the dynamic behavior of variables in the model. Normal model construction follows a pattern of create, examine and recreate, iterating until the model meets users’
requirements. Debugging (making a model simulate properly) and model analysis (investigating output behavior) both play a part in refining the model. Reality check is another analytic to aid in the construction and refinement of a system dynamics model (Sterman, 2000).
Stage.I
Tools Stage.II
Sources Stage.III
Contents Stage.IV
Systems
Trigger of IT for KM
Knowledge worker’s need for end-user tools
Organization’s need for information
Knowledge worker’s need for information automation
Organization’s need for work automation Focus when
applying IT to KM
Make IT available to knowledge workers
Enable knowledge sharing among knowledge workers
Enable sharing of electronic information among knowledge workers
Replace knowledge workers by information systems Dominating
strategy for
KMT Tool strategy Flow strategy Stock strategy Growth
strategy Attitude toward
IT in KM Skeptics Conservatives Early adopters Innovators
Figure 5.5. Characteristics of each stage of knowledge management technology
Wickramasinghe and Silvers (2003) suggest that the orthopedic room represents an ideal environment for the application of a continuous improvement cycle that is dependent upon the intelligence continuum. For those patients with advanced degeneration of their hips and knees, arthtroplasty of the knee and hip represent an opportunity to regain their function. Before the operation ever begins in the operat- ing room, there are a large number of interdependent individual processes that must be completed. Each process requires data input and produces a data output such as patient history, diagnostic test and consultations. The interaction between these data elements is not always maximized in terms of operating room scheduling and completion of the procedure.
The entire process of getting a patient to the operating room for a surgical procedure can be represented by three distinct phases: preoperative, intraoperative and post- operative. The diagnostic evaluation of data and the re-engineering of each of the potentially deficient processes will lead to increased efficiency. For example, many patients are allergic to the penicillin family of antibiotics that are often administered preoperative in order to minimize the risk of infection. For those patients who are allergic, a substitute drug requires a 45 minute monitored administration time as opposed to the much shorter administration time of the default agent. Since the antibiotic is only effective when administered prior to starting the procedure, this often means that a delay is experienced (Wickramasinghe & Silvers, 2003).