Decision Support Systems Theory Research Articles

Online video channel management: An integrative decision support system framework

In the current fragmented media landscape, online video is becoming an important outlet for content dissemination. Online video channels provide content creators a way of organizing content and building an online following through subscriptions and social sharing. This paper describes a decision support systems (DSS) based framework for online video channel management and content creation. The relevant DSS literature was reviewed along with both modeling and behavioral aspects of online videos and video channels. An empirical case study was run on a dataset consisting of views, shares, and subscriptions from over 1000 videos from nine YouTube channels. This paper contributes to DSS theory by proposing a flexible framework for incorporating both behavioral and empirical work into an integrative process for online video content creation. This framework builds on existing data-driven DSS theory, but includes specific entities and processes for online content creation.

Patterns of business intelligence systems use in organizations

Business intelligence (BI) is often used as the umbrella term for large-scale decision support systems (DSS) in organizations. BI is currently the largest area of IT investment in organizations and has been rated as the top technology priority by CIOs worldwide for many years. The most important use patterns in decision support are concerned with the type of decision to be supported and the type of manager that makes the decision. The seminal Gorry and Scott Morton MIS/DSS framework remains the most popular framework to describe these use patterns. It is widely believed that DSS theory like this framework can be transferred to BI. This paper investigates BI systems use patterns using the Gorry and Scott Morton framework and contemporary decision-making theory from behavioral economics. The paper presents secondary case study research that analyzes eight BI systems and 86 decisions supported by these systems. Based on the results of the case studies a framework to describe BI use patterns is developed. The framework provides both a theoretical and empirically based foundation for the development of high quality BI theory. It also provides a guide for developing organizational strategy for BI provision. The framework shows that enterprise and smaller functional BI systems exist together in an organization to support different decisions and different decision makers. The framework shows that personal DSS theory cannot be applied to BI systems without specific empirical support.

The Theory and Method of Agricultural Expert Decision Support System

The Theory and Method of Agricultural Expert Decision Support System

Cognitive biases and decision support systems development: a design science approach

Abstract. This paper presents design science research that aims to improve decision support systems (DSS) development in organizations. Evolutionary development has been central to DSS theory and practice for decades, but a significant problem for DSS analysts remains how to conceptualize the improvement of a decision task during evolutionary DSS development. The objective of a DSS project is to improve the decision process and outcome for a manager making an important decision. The DSS analyst needs to have a clear idea of the nature of the target decision task and a clear strategy of how to support the decision process. Existing psychological research was examined for help with the conceptualization problem, and the theory of cognitive bias is proposed as a candidate for this assistance. A taxonomy of 37 cognitive biases that codifies a complex area of psychological research is developed. The core of the project involves the construction of a design artefact – an evolutionary DSS development methodology that uses cognitive bias theory as a focusing construct, especially in its analysis cycles. The methodology is the major contribution of the project. The feasibility and effectiveness of the development methodology are evaluated in a participatory case study of a strategic DSS project where a managing director is supported in a decision about whether to close a division of a company.

Decision support systems research: Reference disciplines and a cumulative tradition

This study applies factor analysis of an author cocitation frequency matrix derived from a database file consisting of a total of 15,030 cited reference records taken from 692 citing articles. Seven informal clusters of decision support systems (DSS) research subspecialties and reference disciplines were uncovered. Four of them represent DSS research subspecialties—foundations, group DSS, model/data management, and individual differences. Three other conceptual groupings define the reference disciplines of DSS—organizational science, multiple criteria decision making, and artificial intelligence. DSS is a very young academic field and is still growing. DSS has just entered the era of growth after 20 years of research. During the 1990s, DSS research will be further grounded in a diverse set of reference disciplines. Furthermore, DSS is in the active process of solidifying its domain and demarcating its reference disciplines. A DSS theory is imminent in the very near future in some area of DSS research such as model management.

Projecting military housing needs with decision support systems

AbstractThe Department of the Army must provide its personnel with acceptable housing within the vicinity of military installations. To achieve this housing goal, the Army must often enter into agreements for the construction of housing or for leasing of existing housing. Any housing construction or leasing request must be accompanied by a Segmented Housing Market Analysis (SHMA), a time‐consuming and costly forecasting process that requires many manual labor tasks. As described elsewhere, a Housing Analysis System (HANS) was developed to support these tasks on a micro computer with very user‐friendly computer programs. Recently, the original version of HANS has been modified and incorporated into a larger computer system now under development. At the heart of both the original and modified systems is a set of econometric and heuristic programming models that deliver the required analyses and evaluations. This paper describes these models, explains how they drive the modified HANS, and presents the results and benefits of the use of the modified computer system.Biographical Sketch — Guisseppi A. Forgionne is Professor of Information Systems at the University of Maryland Baltimore County (UMBC). Professor Forgionne holds a B.S. in Commerce and Finance, an M.A. in Econometrics, an M.B.A., and a Ph.D. in Management Science and Econometrics. He has published 16 books and approximately 70 research articles and consulted for a variety of public and private organizations on decision support systems theory and applications. Dr. Forgionne has also served as department chair at UMBC, Mount Vernon College, and Cal Poly Pomona. He has received several national and international awards for his work.