Abstract
The continued growth in the volume of available domain and technical data has been facilitated by a corresponding advancement in and communication technology. This information overload can result in inefficient use of time and resources as well as the creation of recommended courses of action that are overruled by the decision makers' judgment and experience. In order to address these problems, multiple knowledge sources and an inference process capable of mirroring the human thought processes (especially judgment and experience) must be available at the right time to the persons or groups needing the knowledge for decision making. Such a concept can be referred to as MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS). Traditional decision support systems (DSS) rely upon decision maker or staff expertise to render knowledge in support of decision making. If the decision maker or staff has insufficient domain or technical expertise to utilize the DSS's embedded models, interpret results, or implement the recommendations, knowledge delivery may be compromised or rendered ineffective. MQTSS can alleviate these support problems and improve knowledge delivery for decision making by reducing knowledge search times, streamlining decision-making tasks, reducing decision time, and promoting appropriate qualitative thinking,. The MQTSS approach theoretically can enhance the decision making process and decision outcomes. This paper attempts to replicate an earlier study by this author to test the theory. First, the MQTSS approach is presented. Next, an system is created to deliver the technology to management. Finally, a simulation experiment is reported that compares the effectiveness of support rendered by a traditional decision support system and the created MQTSS system. The paper closes with conclusions and implications for systems research.
Highlights
We examine the MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS) concept through the use of a software package known as AIS (Academic Information Systems)
The results from Exhibits 2-5 indicate that there were no statistically significant differences between the values for the uncontrollable inputs in the decision support systems (DSS) and MQTSS. These results indicate that the MQTSS did not improve the design phase of decision making
These results indicate that the MQTSS did improve the choice phase of decision making
Summary
Experience and knowledge sharing have greatly enhanced the development of the infrastructure of knowledge management firms like KPMG, Buckman Laboratories, Andersen Consulting and AMS [1] [21] [7] [11]. Qualitative judgment is needed in a timely manner to be able to distinguish between them [7] For this purpose, decision makers and knowledge workers use their interpersonal networks by conferring with colleagues to ask what documents are good for particular applications. Domain knowledge in the intelligence and design phases based upon the judgment and experience of the decision makers must be transferred to the technical tasks involved in choice and implementation. MQTSS can incrementally reduce task lead-time and facilitate a seamless work flow It preempts delays and chaos associated with information overload by restricting data input to that judged to be relevant to the decision makers. This decreased volume is cost-effective. [5]
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