scholarly journals Supporting Decision-Makers: An Expanded Framework

10.28945/2384 ◽  
2001 ◽  
Author(s):  
Daniel Power
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Decision Makers ◽  
Data Driven ◽  
Web Based ◽  
Client Server ◽  
Model Driven ◽  
Mainframe Computer ◽  
External Stakeholders

A conceptual framework for Decision Support Systems (DSS) is developed based on the dominant technology component or driver of decision support, the targeted users, the specific purpose of the system and the primary deployment technology. Five generic categories based on the dominant technology component are proposed, including Communications-Driven, Data-Driven, Document-Driven, Knowledge-Driven, and Model-Driven Decision Support Systems. Each generic DSS can be targeted to internal or external stakeholders. DSS can have specific or very general purposes. Finally, the DSS deployment technology may be a mainframe computer, a client/server LAN, or a Web-Based architecture. The goal in proposing this expanded DSS framework is to help people understand how to integrate, evaluate and select appropriate means for supporting and informing decision-makers.

An Examination of Inter-Organizational Decision Support Systems

2005 ◽  
pp. 248-265
Author(s):  
Daniel J. Power ◽  
Shashidhar Kaparthi
Keyword(s):  
Information Technology ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Model Driven ◽  
External Stakeholders ◽  
Recent Developments

A broad range of Inter-Organizational Decision Support Systems (IODSSs) can be built to support external stakeholders of an organization. This article examines recent developments associated with building and deploying such systems. The IODSS concept is defined, and an information technology architecture for such a system is explored. Examples of current implementations are categorized as communication, data, document, knowledge, and model-driven IODSSs. Further, implementations of IODSSs are categorized as customer- and supplier-focused. Advantages, disadvantages, and current issues associated with IODSSs conclude the discussion.

scholarly journals Arguing with Behavior Influence: A Model for Web-Based Group Decision Support Systems

2019 ◽  
Vol 18 (02) ◽  
pp. 517-553 ◽  
Author(s):  
João Carneiro ◽  
Diogo Martinho ◽  
Goreti Marreiros ◽  
Paulo Novais
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Group Decision ◽  
Support Systems ◽  
Group Decision Support ◽  
Decision Makers ◽  
Group Decision Support Systems ◽  
Web Based ◽  
Dialogue Model ◽  
Face To Face

In this work, we propose an argumentation-based dialogue model designed for Web-based Group Decision Support Systems, that considers the decision-makers’ intentions. The intentions are modeled as behavior styles which allow agents to interact with each other as humans would in face-to-face meetings. In addition, we propose a set of arguments that can be used by the agents to perform and evaluate requests, while considering the agents’ behavior style. The inclusion of decision-makers’ intentions intends to create a more reliable and realistic process. Our model proved, in different contexts, that higher levels of consensus and satisfaction are achieved when using agents modeled with behavior styles compared to agents without any features to represent the decision-makers’ intentions.

scholarly journals Predicting satisfaction: Perceived decision quality by decision-makers in Web-based group decision support systems

2019 ◽  
Vol 338 ◽  
pp. 399-417 ◽  
Author(s):  
João Carneiro ◽  
Pedro Saraiva ◽  
Luís Conceição ◽  
Ricardo Santos ◽  
Goreti Marreiros ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Group Decision ◽  
Support Systems ◽  
Group Decision Support ◽  
Decision Makers ◽  
Group Decision Support Systems ◽  
Decision Quality ◽  
Web Based

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Visualization-Based Decision Support Systems

2013 ◽  
pp. 162-180
Author(s):  
Vicki L. Sauter ◽  
Srikanth Mudigonda ◽  
Ashok Subramanian ◽  
Ray Creely
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Lessons Learned ◽  
Decision Makers ◽  
Specific Information ◽  
Visualization Software ◽  
General Rules ◽  
Visualization Technology ◽  
Visualization Algorithms

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.

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