Urban regeneration (UR) projects encompass complex decision-making processes that usually comprise a great amount of information collected from numerous data sources which may be uncertain, inconsistent or incomplete. Many stakeholders and other actors provide subjective judgments that need to be considered throughout the decision process. Furthermore, most factors involved in regeneration proj...

This paper integrates a number of strands of a long-term project that is critically analysing the academic field of decision support systems (DSS). The project is based on the content analysis of 1093 DSS articles published in 14 major journals from 1990 to 2004. An examination of the findings of each part of the project yields eight key issues that the DSS field should address for it to contin...

The view of decision-making that has informed most decision support systems research has been Simon’s process theory. Further, the majority DSS work has focussed on the choice phase of Simon’s model of decision-making. Decision bias theory does not focus on the choice process but considers the possibility of systematic and persistent errors in all phases of judgement and decision-making. Decisi...

a r t i c l e i n f o We study 56 subjects of varying knowledge in a preferential choice task, aided by one of two Decision Support Systems (DSS) of different restrictiveness: an eliminative tool (ELIM) and a parametric search tool (PS). Using a novel measure for performance based on Data Envelopment Analysis (DEA), we find that the gains due to effort are greater with the less restrictive DSS....

Business intelligence (BI) is a growing area of applications that go beyond the reporting and analysis available with transaction processing systems, which are more frequently being implemented with enterprise software. The BI space shares many of the same objectives as previously examined decision support systems (DSS). A number of DSS frameworks were formulated that describe their characteris...

Decision support systems are powerful technologies for complex decision making and problem solving. However, constructing an accurate and interpretable decision support system (DSS) for any domain is a challenge. In this paper, a novel hierarchical co-evolutionary fuzzy system called HiCEFS is presented that can autonomously derive a fuzzy rule-based DSS from exemplar data. Most of the importan...

Two objectives in the design of decision support systems (DSS) are to improve decision-malting performance and to use DSS modeling forms that are natural, that is, to adopt modeling paradigms that are congruent with decision makers’ conceptual models of decision tasks. By accomplishing the latter objective, a DSS should enjoy better conceptual ease of use and face validity. However, past resear...

Decision support systems (DSS) have been used in many applications to speed up decision making. This article will discuss the implementation of DSS for decision-making for scholarships "bidik misi". In terms of processing data related to the bidik misi scholarship are accordance with the regulations set by UBD. To facilitate the selection process of bidik misi scholarship then the authors used ...

Although research exists in areas of decision support, behavioral ethics, as well as personal differences and decision making, little is understood about the processes at work at the intersection of these areas. We believe that such research will support developing decision support systems for ethics (DSSE). We lay part of the foundation to support the development of next generation DSS that fi...

Assessing the value of decision support systems (DSS) in organizations is an important line of research. Traditionally, researchers adopt user satisfaction and decision performance as two measures of system success. In some cases, however, the use of DSS may not be driven by satisfaction. Instead, avoiding possible decision errors may be the reason for adoption, which indicates that regret avoi...