Francis K. Andoh-Baidoo
State University of New York at Brockport
Jon Blue
University of Delaware
SIG-DSS Pre-ICIS 2006 Research Workshop
December 10, 2006
Milwaukee, WI

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Problem Statement
Theoretical Framework
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Decision Making and Decision Support Systems (DSS)
Data Warehouse
Knowledge Management System
Organizational Memory Information System (OMIS)
Knowledge Spiral (Nonaka & Takeuchi, 1995)
Proposed Modern Decision Support System Approach - OMKS
Knowledge Conversion in OMKS
Implications for Research and Practice
Conclusions
December 10, 2006 Andoh-Baidoo and Blue 1

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Researchers have recommended that organizations eliminate their silo systems by consolidating their data, information, and knowledge repositories to enable effective and efficient decision making. Unfortunately, most organizations have not realized this end
The acquisition, storage, and utilization of tacit knowledge is difficult
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 Decision Making and Decision Support
Systems (DSS)
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Modern DSS are commissioned to support all four phase of the decision making process: intelligence, design, choice, and implementation
(Simon, 1955)
Data Warehouses, Knowledge Systems, and
Organizational Memory Information Systems support decision making
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 Data Warehouse
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Defined as “…a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management’s decision-making process” (Inmon, p. 1)
 Typically, On-Line Analytical Processing
(OLAP), Data Mining, and Knowledge Discovery tools are used to support decision making processes
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 Knowledge Management System
 Repository for explicit & tacit knowledge
 Explicit knowledge – systematic and can be expressed formally as language, rules, objects, symbols, or equations
 Tacit knowledge – includes beliefs, perspectives, and mental models ingrained in a person’s mind
 Tacit knowledge can be articulated, captured, and represented (Nonaka, Takeuchi, & Umemoto, 1996;
Polyshyn, 1981)
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 Organizational Memory Information Systems
(OMIS)
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Integrated knowledge based IS with culture, history, business processes, and human memory attributes (Hackbarth, 1998)
Facilitate Organizational Learning: Individual learning, learning through direct communication, and learning using a knowledge repository (Heijst et al., 1997)
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 Knowledge Spiral (Nonaka & Takeuchi, 1995)
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 Scenarios
 Ontology
 Metadata
 Data / Knowledge Repositories
 Knowledge Conversion
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 A Scenario is a sequence of hypothetical (but mimicking real) situations encountered by a domain expert, together with the intermediate responses/actions (Yu-N & Abidi, 2000)
 Ontology is a common and shared understanding of some domain that is capable of being communicated across people and systems (Benjamins et al., 1998)
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 Ontology can be used with Scenarios to standardize the acquisition of tacit knowledge
(Yu-N & Abidi, 2000)
 Ontology-based metadata represents a common global metadata
 Ontology-based metadata addresses the issues of data and semantic heterogeneity
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Proposed
Organizational
Memory and
Knowledge
System
Organization’s
Individuals
Scenarios/ Organizational
Ontology
To Capture
Tacit Knowledge
December 10, 2006
Marketing
Sales
Manufacturing
Human
Resources
ETL + Organizational
Ontology
Organization’s/External
Databases
Summarized
Data
Knowledge
Repository
Data
Warehouse
Knowledge
Ontological
Metadata
Analysis Tools
Admin Tools
Development
Tools
Tools to Access Data
Edit/Query Interface/Browser
Aggregated
Data
Andoh-Baidoo and Blue 11

Knowledge Conversion in OMKS
 Externalization (tacit to explicit)
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Scenario based acquisition
Facilitates tacit to explicit knowledge by using mathematical models (Nemati et al., 2002)
 Stored as explicit mathematical inequalities
 Canonical model formulations with links to relational tables in the DSS
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Knowledge Conversion in OMKS (con’t).
 Socialization (tacit to tacit)
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Ontology facilitates the common vocabulary for knowledge worker communication
Storage of digitized films of physical demonstration for viewing by any organization members (with verbal explanations that explain the process)
Kinematics - individual sited with probes and a system records the movements of the person
(Nemati et al., 2002)
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Knowledge Conversion in OMKS (con’t).
 Combination (explicit to explicit)
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Explicit knowledge is reconfigured
 Valid knowledge can be used to modify existing knowledge
AI-based data mining on the output from brainstorming sessions
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Knowledge Conversion in OMKS (con’t).
 Internalization (explicit to tacit)
 Knowledge workers improve their work activities through the shared knowledge (modification of the mental model)
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 Research
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More design science research needed on how to develop modern DSS using the proposed approach
Theory based behavioral research needed on the organizational impact of the proposed approach
Further research needs an integrated team of
DSS, OMIS, and Data Warehousing scholars
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 Practice
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Organizations may benefit from the exploration of integrating existing Data Warehousing and
Organizational Memory Information System
Organizations using the proposed framework can enhance decision making and organizational learning
Consultants may be called upon to study the problems with integrating systems in the proposed framework
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Researchers have suggested that integrating knowledge management and decision support systems can enhance decision making
We have proposed a framework for developing modern DSS that combines functional features of data warehousing and organizational memory information systems
Framework uses scenarios to capture tacit knowledge and ontology for standardization
 Such an approach has the potential to enhance decision making and organizational learning
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Benjamins, V.R., Fensel, D., & Perez, A.G. (1998). Knowledge Management through Ontologies.
In
Proceedings of the Second International Conference of Practical Aspects of Knowledge
Management (PAKM 98), October 29-30 .
Hackbarth, G. (1998). The Impact of Organizational Memory on IT Systems, In Proceedings of the
Fourth Americase Conference on Information Systems, E. Hoadley and I. Benbasat (eds)., pp. 588-
590.
Heijst, G., Spek, R., & Kruizinga, E. (1997). Corporate memories as a tool for knowledge management. Expert Systems With Applications, 13(1), 41–54.
Inmon, W. (1995). What is a Data Warehouse?
Prism Tech Topic, Vol.1, No. 1.
Nemati, H.R., Steiger, D.M., Iyer, L.S., & Hershel, R.T. (2002). Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing, Decision Support Systems , Volume 33, Issue 2, June, 143-161.
Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company, How Japanese companies manage the dynamics of innovation , Oxford University Press, New York.
Nonaka, I., Takeuchi, H., & Umemoto K. (1996). A theory of organizational knowledge creation,
International Journal of Technology Management, 11(7/8), 833 – 845.
Polanyi, M. (1966). The Tacit Dimension . Routledge and Kegan Paul, London, UK, 1966.
Simon, H.A. (1955). A Behavioral Model of Rational choice. Quarterly Journal of Economics, Vol.
69, pp. 99-118.
Yu-N, C., Abidi, S.S.R. (2000). A Scenarios Mediated Approach for Tacit Knowledge Acquisition and Crystallisation: Towards Higher Return-On-Knowledge and Experience , In Proceedings of the
Third International Conference on Practical Aspects of Knowledge Management (PAKM2000)
Basel, Switzerland, 30-31 Oct. 2000.
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