An Architecture for the Integration of Decision Making Support Functionalities

2011 ◽  
pp. 1-19 ◽  
Author(s):  
Guisseppi A. Forgionne
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Intelligent Systems ◽  
Support Systems ◽  
Decision Making Process ◽  
Executive Information Systems ◽  
Robust Decision Making ◽  
Advantages And Disadvantages ◽  
The Individual ◽  
Decision Making Support

Various information systems have evolved to support the decision making process. There are decision support systems (DSS), executive information systems (EIS), artificially intelligent systems (AIS), and integrated combinations of these systems. Each of the individual systems supports particular phases and steps of the decision making process, but none of the individual systems supports the entire process in an integrated and complete manner. The integrated systems alleviate the support deficiencies, and each of the integration approaches has specific advantages and disadvantages. By studying these advantages and disadvantages, researchers and practitioners can better design, develop, and implement robust decision making support systems. This chapter facilitates such study by presenting and illustrating the underlying information system architectures for robust decision making support.

Decision-Making Support Systems

2011 ◽  
pp. 978-984
Author(s):  
Guisseppi Forgionne ◽  
Manuel Mora ◽  
Jatinder N.D. Gupta ◽  
Ovsei Gelman
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Support Systems ◽  
Original Version ◽  
Executive Information Systems ◽  
Intelligent Decision Making ◽  
Knowledge Based ◽  
Challenges And Opportunities ◽  
Research Findings ◽  
Decision Making Support

Decision-making support systems (DMSS) are computerbased information systems designed to support some or all phases of the decision-making process (Forgionne, Mora, Cervantes, & Kohli, 2000). There are decision support systems (DSS), executive information systems (EIS), and expert systems/knowledge-based systems (ES/KBS). Individual EIS, DSS, and ES/KBS, or pair-integrated combinations of these systems, have yielded substantial benefits in practice. DMSS evolution has presented unique challenges and opportunities for information system professionals. To gain further insights about the DMSS field, the original version of this article presented expert views regarding achievements, challenges, and opportunities, and examined the implications for research and practice (Forgionne, Mora, Gupta, & Gelman, 2005). This article updates the original version by offering recent research findings on the emerging area of intelligent decision-making support systems (IDMSS). The title has been changed to reflect the new content.

Decision-Making Support Systems

2005 ◽  
pp. 759-765 ◽  
Author(s):  
Guisseppi Forgionne ◽  
Manuel Mora ◽  
Jatinder N.D. Gupta ◽  
Ovsei Gelman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Support Systems ◽  
Decision Makers ◽  
Knowledge Based Systems ◽  
Executive Information Systems ◽  
Knowledge Based ◽  
Computer Based ◽  
Decision Making Support

Decision-making support systems (DMSS) are specialized computer-based information systems designed to support some, several or all phases of the decision-making process (Forgionne et al., 2000). They have the stand-alone or integrated capabilities of decision support systems (DSS), executive information systems (EIS) and expert systems/knowledge based systems (ES/KBS). Individual EIS, DSS, and ES/KBS, or pair-integrated combinations of these systems, have yielded substantial benefits for decision makers in real applications.

Updated Architectures for the Integration of Decision Making Support Functionalities

2011 ◽  
pp. 3884-3889 ◽  
Author(s):  
Guisseppi A. Forgionne
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Intelligent Systems ◽  
Data Access ◽  
Robust Decision Making ◽  
Intelligent Support ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
New Literature ◽  
Decision Making Support

Information systems research continues to examine ways to improve support for decision making. The evolution from simple data access and reporting to complex analytical, creative, and artificially intelligent support for decision making persists (Holsapple & Whinston, 1996). In the evolution, existing information systems still, and new intelligent systems have been created to, provide the desired decision making support. By studying the existing, and new, systems’ characteristics, advantages, and disadvantages, researchers and practitioners can better design, develop, and implement robust decision making support systems (Kumar, 1999). The original article facilitated such study by presenting and illustrating the underlying information system architectures for robust decision making support (Forgionne, 2005). This article updates the original by offering additional contributions to the subject. New literature on intelligent decision making support is examined, and the relevant findings are discussed. The title has been modified slightly to reflect the updates.

Evaluation of Decision-Making Support Systems

2011 ◽  
pp. 131-140
Author(s):  
Gloria E Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Support Systems ◽  
The Internet ◽  
Dynamic Data ◽  
Human Decision ◽  
Broad Classification ◽  
Specific Contribution ◽  
Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

Evaluation of Decision-Making Support Systems

2011 ◽  
pp. 320-328 ◽  
Author(s):  
Gloria E. Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Support Systems ◽  
The Internet ◽  
Dynamic Data ◽  
Human Decision ◽  
Broad Classification ◽  
Specific Contribution ◽  
Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

A Comparison of Implementation Resistance Factors for DMSS Versus Other Information Systems

2004 ◽  
pp. 315-332 ◽  
Author(s):  
Kristina Setzekorn ◽  
Vijayan Sugumaran ◽  
Naina Patnayakuni
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Business Processes ◽  
Support Systems ◽  
Business Environment ◽  
Executive Information Systems ◽  
Resistance Factors ◽  
Knowledge Based ◽  
Other Information ◽  
Effective Decision Making

Effective decision-making within and across organizations is of strategic importance as the global business environment becomes more complex. Business processes and their related computer based information systems (CBIS) must support integrated decision-making. While decision support systems (DSS), executive information systems (EIS), and knowledge-based systems (KBS) have been independently used to support problem solving and decision making activities, they are still not widely implemented and accepted by a broad spectrum of organizations. Identifying the reasons for the lack of widespread use, as well as integration of these technologies would enable organizations to better design and implement these support systems. Using 41 narratives, we have compared decision-making support systems (DMSS) resistance factors with those of other CBIS to better understand these factors and their impact on DMSS implementation.

Using Narratives to Convey Knowledge in Decision Making Support Systems

2011 ◽  
pp. 257-270
Author(s):  
Lee A. Freeman
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Support Systems ◽  
Research Articles ◽  
Future Research ◽  
Multiple Streams ◽  
Present Information ◽  
Data Tables ◽  
Work Done ◽  
Decision Making Support

Information systems, and specifically decision making support systems, present information to users in a variety of modes—raw data, tables, graphs, and others. Rarely, if ever, does an information system present information to users in a narrative or story-based format. The last three decades have seen a variety of research articles that have presented an argument, an example, or a reference to what can be termed narrative-based information systems (NBIS). This chapter traces this history as they contribute to the development of NBIS. This chapter traces this history as they contribute to the development of NBIS. Previous work has come from multiple disciplines and multiple streams within Information Systems. To date, there has been very little work done in this area, and it is hypothesized that the reason is in part due to the multi-disciplinary approach and the lack of a unified effort. In order to further the efforts of this area of research, a conceptual model of the history is developed. The paper concludes with areas for future research.

The “Mental Revolution” of the Public Procurement Specialist

2012 ◽  
pp. 1-16 ◽  
Author(s):  
Alexandru V. Roman
Keyword(s):  
Decision Making ◽  
Support Systems ◽  
Public Procurement ◽  
Individual Level ◽  
The Public ◽  
The Individual ◽  
Decision Making Support

This chapter suggests an original perspective for delineating the role played by procurement specialists within the context of the efforts to redefine digital public procurement as a major pylon in the transformation of governance. Although in the last two decades scholars have provided an abundance of quality academic accounts addressing the possible transformative benefits of e-procurement, more often than not, public procurement specialists remain a mere afterthought within such discussions. In this chapter, it is argued that the digitalization of public procurement will sustain the desired transformative returns only if these efforts are accompanied by a reformative evolution of public procurement professionals. Paradoxically, transformation at the individual level is found to be the key element for instituting genuine changes and effectively employing digital decision-making support systems in public procurement.

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