scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

OBJECTIFICATION OF FUZZY INFERENCE IN DECISION-MAKING PROBLEMS

2021 ◽  
Author(s):  
L. P. Vershinina ◽  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Statistical Models ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Practical Application ◽  
Inference Process ◽  
Quality Of Information

The basis of modern decision support systems is not so much analytical and statistical models as the practical application of specialists ‘ knowledge. Such systems are based on fuzzy technologies. The quality of decisions made depends on how accurately the quality of information is reflected in the fuzzy inference process. Ways to improve the objectivity of fuzzy inference at the stages of fuzzification, aggregation, activation, and accumulation are proposed.

The Information Quality of Databases

2005 ◽  
pp. 281-285 ◽  
Author(s):  
InduShobha Chengalur-Smith ◽  
M. Pamela Neely ◽  
Thomas Tribunella
Keyword(s):  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Information Quality ◽  
Support Systems ◽  
Transaction Processing ◽  
Quality Of Information ◽  
Transaction Processing Systems

A database is only as good as the data in it. Transaction-processing systems and decision-support systems provide data for strategic planning and operations. Thus, it is important to not only recognize the quality of information in databases, but also to deal with it. Research in information quality has addressed the issues of defining, measuring and improving quality in databases; commercial tools have been created to automate the process of cleaning and correcting data; and new laws have been created that deal with the fallout of poor information quality. These issues will be discussed in the next sections.

scholarly journals Decision and decision makers

2010 ◽  
Author(s):  
Anuta Porutiu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Technologies ◽  
Support Systems ◽  
Policy Makers ◽  
Systemic Analysis ◽  
New Information ◽  
Enterprise Success

In the current economic context, decision making requires complex and multiple actions on the part of the policy makers, who are more challenged than in previous situations, due to the crisis that we are facing. Decision problems cannot be solved by focusing on manager’s own experience or intuition, but require constant adaptation of the methods used effectively in the past to new challenges. Thus, a systemic analysis and modeling of arising issues is required, resulting in the stringent use of Decision Support Systems (DSS), as a necessity in a competitive environment. DSS optimize the situation by getting a timely decision because the decision making process must acquire, process and interpret an even larger amount of data in the shortest possible time. A solution for this purpose is the artificial intelligence systems, in this case Decision Support Systems (DSS), used in a wider area due to expansion of all the new information technologies in decisionmaking processes. These substantial cyber innovations have led to a radical shift in the relationship between enterprise success and quality of decisions made by managers.

Group Support Systems as Tools for HR Decision Making

2011 ◽  
pp. 1087-1095
Author(s):  
James Yao ◽  
John Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Human Resource ◽  
Financial Management ◽  
Support Systems ◽  
Group Support Systems ◽  
Group Support ◽  
Soft Data

In the late 1960s, a new type of information system came about: model-oriented DSS or management decision systems. By the late 1970s, a number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semistructured problems. These diverse systems were all called decision support systems (DSS). From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. DSS could support operations, financial management, and strategic decision making. Group decision support systems (GDSS) which aim at increasing some of the benefits of collaboration and reducing the inherent losses are interactive information technology-based environments that support concerted and coordinated group efforts toward completion of joint tasks (Dennis, George, Jessup, Nunamaker, & Vogel, 1998). The term group support systems (GSS) was coined at the start of the 1990s to replace the term GDSS. The reason for this is that the role of collaborative computing was expanded to more than just supporting decision making (Patrick & Garrick, 2006). For the avoidance of any ambiguities, the latter term shall be used in the discussion throughout this article. Human resources (HR) are rarely expected like other business functional areas to use synthesized data because HR groups have been primarily connected with transactional processing of getting data into the system and on record for reporting and historical purposes (Dudley, 2007). For them soft data do not win at the table; hard data do. Recently, many quantitative or qualitative techniques have been developed to support human resource management (HRM) activities, classified as management sciences/operations research, multiattribute utility theory, multicriteria decision making, ad hoc approaches, and human resource information systems (HRIS) (Byun, 2003). More importantly, HRIS can include the three systems of expert systems (ES), decision support systems (DSS), and executive information systems (EIS) in addition to transaction processing systems (TPS) and management information systems (MIS) which are conventionally accepted as an HRIS. As decision support systems, GSS are able to facilitate HR groups to gauge users’ opinions, readiness, satisfaction, and so forth, increase their HRM activity quality, and generate better group collaborations and decision makings with current or planned HRIS services. Consequently, GSS can help HR professionals exploit and make intelligent use of soft data and act tough in their decision-making process.

scholarly journals Appropriate Information Technology In Emerging Economies: An Application For Strategic Decision-Making In Manufacturing Industries

2011 ◽  
Vol 3 (4) ◽  
Author(s):  
Mehdi Beheshtian Ardakani ◽  
Mohsen Modarres ◽  
Ahmad Ispahani
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Emerging Economies ◽  
Support Systems ◽  
Strategic Decision ◽  
Top Executives ◽  
The Impact

Competing in global marketplace has pressured managers respond to shifting market trends by increasing product quality, business process reengineering, and decreasing time to market for new products. Within emerging economies top executives have realized that adoption of appropriate information technologies such a decision support systems (DSS) and group decision support systems (GDSS) have led to changes in the existing organizational structure and communication mechanisms. This paper explores the advantages and constraints of DSS and GDSS in formulating manufacturing strategies in emergent economies. We argued that to fit appropriate information technology to organizational design top executive would benefit from strategic information systems planning process. This process enables top executives to invest in appropriate information system that fits their structural arrangements and organizational culture. Moreover, we explored the impact of DSS and GDSS on executive decision-making capabilities. We also explored the methodology for implementation of appropriate information systems in manufacturing firms in emergent economies.

Geospatial Decision Support Systems

2020 ◽  
pp. 163-177
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Development Planning ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Interactive Decision Support

The chapter is on the geospatial decision support systems. Challenges arise when simple GIS is used to support complex problems encountered at higher level, strategic decision-making, and long-term development planning. In this case, SDI can be more valuable. The chapter presents the perspective of information systems for decision support taking into account the following: the levels of decisions and the process of decision making. Trends on the tools and framework for interactive decision support systems are discussed focusing on geospatial decision support systems based on GIS and SDI.

scholarly journals An Object-Oriented Frameworks-based Architecture for Decision Support Systems

1998 ◽  
Vol 1 (1) ◽  
Author(s):  
Karin Becker ◽  
François Bodart
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Information Technologies ◽  
Support Systems ◽  
Rapid Development ◽  
Object Oriented ◽  
Coarse Grained ◽  
Reusable Components

Reusability is considered to be the key for achieving productivity and quality in software, and much has been claimed about the particular contributions of the object-oriented paradigm towards the achievement of these goals. Object-oriented frameworks are coarse-grained reuse units, composed of a set of classes specifically designed to be refined and used as a group. In this paper, we discuss the nature of frameworks necessary to build a particular type of systems, namely Decision Support Systems (DSS), and their organization in a generic OO DSS multi-layer architecture. DSS are systems intended to improve the effectiveness of decision making, but information technologies can only have a major impact on decision making if techniques allowing the easy and rapid development of DSS are available. Much benefit is expected in terms of easiness and rapidity of development by constructing DSS from domain-oriented reusable components, as well as in terms of quality of DSS in this way developed. 

The Role of Decision Support Systems in Attenuating Racial Biases in Healthcare Delivery

2020 ◽  
Vol 66 (11) ◽  
pp. 5171-5181 ◽  
Author(s):  
Kartik K. Ganju ◽  
Hilal Atasoy ◽  
Jeffery McCullough ◽  
Brad Greenwood
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Healthcare Delivery ◽  
Care Quality ◽  
Healthcare Management ◽  
Racial Biases ◽  
Black Patients

Although significant research has examined how technology can intensify racial and other outgroup biases, limited work has investigated the role information systems can play in abating them. Racial biases are particularly worrisome in healthcare, where underrepresented minorities suffer disparities in access to care, quality of care, and clinical outcomes. In this paper, we examine the role clinical decision support systems (CDSS) play in attenuating systematic biases among black patients, relative to white patients, in rates of amputation and revascularization stemming from diabetes mellitus. Using a panel of inpatient data and a difference-in-difference approach, results suggest that CDSS adoption significantly shrinks disparities in amputation rates across white and black patients—with no evidence that this change is simply delaying eventual amputations. Results suggest that this effect is driven by changes in treatment care protocols that match patients to appropriate specialists, rather than altering within physician decision making. These findings highlight the role information systems and digitized patient care can play in promoting unbiased decision making by structuring and standardizing care procedures. This paper was accepted by Stefan Scholtes, healthcare management.

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