Usage Of Decision Support Systems Research Articles

AI-Driven Decision Support System Innovations to Empower Higher Education Administration

This study explores the utilization, perceptions, and impacts of Decision Support Systems (DSS) in higher education administration. With a focus on DSS, a cross-sectional survey was conducted among higher education administrators from various institutions. The findings underscore the essential role of DSS in higher education administration, with administrators reporting significant utilization and praising their effectiveness and user-friendliness. The study reveals the positive influence of DSS on strategic planning, enrollment management, resource allocation, and student success initiatives. Moreover, it demonstrates the association between DSS usage and favorable outcomes, including increased efficiency and perceived positive consequences. However, persistent challenges such as data quality issues, privacy concerns, and resistance to change highlight the need for improved data management strategies, ethical considerations, and change management approaches. These findings contribute to the ongoing discourse on the transformative potential of DSS in higher education administration and provide valuable insights for businesses seeking to enhance decision-making, resource allocation, and data-driven initiatives. The innovative integration of AI in DSS for higher education administration represents a paradigm shift in decision-making processes, offering unprecedented opportunities for improvement and innovation.

Context-dependent preferences for a decision support system's level of automation

Many organizations use decision support systems (DSS) to support DSS users in their daily work demands (e.g., high workload, insufficient information, ambiguous situations). A key question regarding their interaction is how the decision-control is divided between the DSS and the user, represented by the system's level of automation (LoA). To investigate the need for an adaptable DSS where users can manually adjust the LoA across situations, we used a vignette design to examine whether users prefer different LoA in different situations (i.e., six situational criteria, each manipulated by two specifications; e.g., low vs. high workload). In the twelve vignettes, the 116 participants should imagine working in an emergency control-center—a setting they were familiar with from previous experiments. Our results showed significant differences between the two corresponding vignettes, indicating that users prefer different LoA across situations. However, after controlling for the participants' overall preference for a situation-independent baseline LoA, the significant differences between all paired vignettes disappear. Our results have implications for whether situational or individual criteria are more important regarding LoA preferences, adaptable DSS, and for human-centered design based on user profiles. We discuss our findings in relation to the broader literature on trust and acceptance of DSS.

The development of a novel decision support system for regional land use planning for brownfield land

Digital tools, particularly specialised decision support systems (DSSs), can be utilized to assist in the complex process of brownfield redevelopment. Existing brownfield DSSs typically focus on site-specific, late-stage applications, and socioeconomic factors are often overlooked. In this paper, we present a novel DSS aimed at providing support for early-stage, city region-scale brownfield land use planning and redevelopment. The proposed DSS is a prototype WebGIS application that enables land use planners and other brownfield regeneration professionals to examine a region and a set of sites during the initial planning phase for brownfield redevelopment. The DSS includes three bespoke modules comprising: (1) Land Use Potential (residential, commercial, and public open space), (2) risks posed by contamination and geotechnical hazards, (3) data pertinent to brownfield economic viability assessments. We outline a use case for this DSS, developed through comprehensive user-requirements gathering, and subsequently describe the techniques employed to construct the DSS modules and user interface. Finally, we present the results of user testing, wherein case-study stakeholders assessed the DSS. The feedback obtained during user testing aided in the identification of areas for improvement with regard to the functionality, usability, and effectiveness of the DSS in supporting decision-makers. The feedback was utilized to implement iterative improvements to the DSS and to plan future developments for the prototype DSS.

An Experimental Evaluation of a Satellite Data Decision Support System for Flood Mitigation Efforts in Indonesia

Decision support systems (DSS) incorporating satellite data can potentially augment the understanding of flood management decision makers in Indonesia. To mitigate mismatches between DSS functionality and stakeholder objectives that have hindered previous efforts, a DSS is developed using a novel stakeholder engagement process. A pilot study of Boston-area participants (n = 20) assessed whether DSS users had improved understanding of historical trends and policy impacts compared to control group participants receiving a briefing. Participants’ understanding and projection of environmental, socioeconomic, and policy trends were assessed via scored questions and compared between groups using a Wilcoxon sign rank test. Higher understanding scores across categories were obtained by DSS users compared to the control group. Improved understanding of environmental, socioeconomic, and policy factors can be gained from a satellite data DSS developed using our novel process. Increased confidence in the DSS was gained ahead of evaluations with Indonesian end-user decisionmakers.

Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

A richer approach to studying Decision Support System (DSS) interactions is required to understand and predict the nature of actual use in the workplace. We used questionnaire and interview techniques to examine workers’ experiences relating to DSS use in naturalistic settings. We aimed to: 1) Reveal what workers perceive to be the most important factors when deciding whether to accept support from a DSS and 2) Elicit patterns that emerge from DSS users’ recounted experiences using the systems, which may impact their future use. Current and prospective DSS users ( N = 93) from numerous industries responded to a questionnaire relating to the factors they perceive to influence their use of DSSs. Subsequently, a retrospective interview protocol was employed to investigate the experiences of a subset of DSS users ( N = 10). The questionnaire results underscore a range of factors considered to be very important to the acceptance of DSSs (i.e. decision quality; decision importance; decision risk; historical accuracy; decision accountability; and system comprehension). Further, a series of interconnected themes relating to workers’ use of DSSs were identified from the interview transcripts using thematic analysis. We discuss how these issues may impact workers’ intentions to use DSSs in the workplace, and advocate for the use of naturalistic decision-making techniques to study technology acceptance.

Diagnostic concordance among nursing clinical decision support system users: a pilot study.

To analyze the nursing diagnostic concordance among users of a clinical decision support system (CDSS), The Electronic Documentation System of the Nursing Process of the University of São Paulo (PROCEnf-USP®), structured according to the Nanda International, Nursing Intervention Classification and Nursing Outcome Classification (NNN) Taxonomy. This pilot, exploratory-descriptive study was conducted from September 2017 to January 2018. Participants were nurses, nurse residents, and nursing undergraduates. Two previously validated written clinical case studies provided participants with comprehensive initial assessment clinical data to be registered in PROCEnf-USP®. After having registered the clinical data in PROCEnf-USP®, participants could either select diagnostic hypotheses offered by the system or add diagnoses not suggested by the system. A list of nursing diagnoses documented by the participants was extracted from the system. The concordance was analyzed by Light's Kappa (K). The research study included 37 participants, which were 14 nurses, 10 nurse residents, and 13 nursing undergraduates. Of the 43 documented nursing diagnoses, there was poor concordance (K = 0.224) for the diagnosis "Ineffective airway clearance" (00031), moderate (K = 0.591) for "Chronic pain" (00133), and elevated (K = 0.655) for "Risk for unstable blood glucose level" (00179). The other nursing diagnoses had poor or no concordance. Clinical reasoning skills are essential for the meaningful use of the CDSS. There was concordance for only 3 nursing diagnoses related to biological needs. The low level of concordance might be related to the clinical judgment skills of the participants, the written cases, and the sample size.

A web-based integrated Bayesian decision support system for rangeland management in semi-arid watersheds

A Bayesian web-based Decision Support System (DSS) was designed and developed to assist various stakeholders in improving biophysical and socio-economic conditions in a stressed and fragile watershed by systematically evaluating current watershed characteristics, introducing feasible solutions as well as choosing the best ones. The need to deliver a flexible DSS has been the motivation to look for a decision toolbox accommodating the concerns and priorities of a wide range of stakeholders as the potential users. This assists the users to make informed decisions based on quantified potential outcomes. The DSS main components include a database management system, a model base management system, multi-attribute decision-making models, and a graphical user interface. This prototype DSS provides a brief introduction of a pilot watershed characterized with semi-arid conditions in the Mid-north of Iran and then presents the problem structure using the DPSIR approach. A set of vegetation-based solutions and their spatial distribution considering some scenario rules are provided to the DSS users. The various impacts of the proposed solutions are predicted using several models in the Bayesian network modeling context integrating qualitative and quantitative knowledge and data. The DSS developed in this study facilitates knowledge transfer and involvement of stakeholders including practitioners and local communities in the decision making process. This DSS has been designed into a web-based and open-source framework for easier and inclusive access. The DSS can be tailored to fit a broader class of environmental problems.

Effects of reliability indicators on usage, acceptance and preference of predictive process management decision support systems

Despite the growing availability of data, simulation technologies, and predictive analytics, it is not yet clear whether and under which conditions users will trust Decision Support Systems (DSS). DSS are designed to support users in making more informed decisions in specialized tasks through more accurate predictions and recommendations. This mixed-methods user study contributes to the research on trust calibration by analyzing the potential effects of integrated reliability indication in DSS user interfaces for process management in first-time usage situations characterized by uncertainty. Ten experts specialized in digital tools for construction were asked to test and assess two versions of a DSS in a renovation project scenario. We found that while users stated that they need full access to all information to make their own decisions, reliability indication in DSS tends to make users more willing to make preliminary decisions, with users adapting their confidence and reliance to the indicated reliability. Reliability indication in DSS also increases subjective usefulness and system reliability. Based on these findings, it is recommended that for the design of reliability indication practitioners consider displaying a combination of reliability information at several granularity levels in DSS user interfaces, including visualizations, such as a traffic light system, and to also provide explanations for the reliability information. Further research directions towards achieving trustworthy decision support in complex environments are proposed.

Influencing crowding at locations with decision support systems: The role of information timeliness and location recommendations

To target crowding at locations, decision support systems (DSS) increasingly feature crowding information (CI) to indicate how much of a location's available capacity is occupied. Yet, little is known about how and why the timeliness of such CI (e.g., “updated just now”) influences users' selections of differently crowded locations and the effectiveness of location recommendations. Addressing this knowledge gap is, however, important to understand how to design DSS interfaces that prevent (over)crowding and improve related DSS reuse intentions. Drawing on construal level theory, we applied a mixed-methods approach comprising a quantitative and a complementary qualitative study. First, we conducted an online experiment in which 171 participants selected between differently crowded bars. The quantitative data provides evidence that high (vs. low) timeliness of CI leads users to select less crowded bars and raises users' DSS reuse intentions. Yet, the effect of high (vs. low) timeliness of CI on location selection is unexpectedly cancelled out when location recommendations are displayed. In subsequent qualitative interviews with DSS users we find that even though high (vs. low) timeliness of CI affords more conscious elaboration on CI's costs and benefits and increased trusting beliefs, present (vs. absent) location recommendations decrease one's cognitive effort and are more influential than timeliness of CI with regard to the actual location selection. Furthermore, high (vs. low) timeliness of CI enhances positive user perceptions, explaining the increased DSS reuse intentions. Overall, we provide novel insights on the role of timeliness of information for DSS to influence crowding behavior.

Decision Support Systems Utilization in Forestry: Environmental Aspect

In this paper, the actuality of utilization of informational technology in forestry is underlined. There is noted, that the technical and technological modernization within the frameworks of “Industry 4.0” for this sector is required. There are described methods, which give the potential to reduce forest machines' negative influence on the environment. There is emphasized, that the maximal damage to soils is inflicted by wheel propellers. There is proposed to use decision support systems (DSS) for the control process improvement and simplification on the territory, intended for cutting. There is given the system structure, main elements, and theirs principles of functioning are described. There are proposed some performance characteristics for the measurement of changes in consequence of the implementation of the depicted DSS, among them: response time on an incident from the moment of detection, situation analysis time, clearing time, and track depth, created by forest machine. There is made the simulation of an array of situations, stressing an efficiency of DSS use, such as: a) Can one reduce a soil purification time from oil products? b) Can one prevent an exceeding of the required track depth level? There is considered an impact of this DSS on existing indices of “Industry 4.0”. There are substantiated conclusions about the reasonability and efficiency of this DSS usage in forestry. There is noticed, that the DSS application to some extent allows preventing the erosion of forest soils, also to warn a diminishing of fertility, created by the irrational use and chemical pollution.

Impact of a Novel Diabetes Support System on a Cohort of Individuals With Type 1 Diabetes Treated With Multiple Daily Injections: A Multicenter Randomized Study.

Achieving optimal glycemic control for many individuals with type 1 diabetes (T1D) remains challenging, even with the advent of newer management tools, including continuous glucose monitoring (CGM). Modern management of T1D generates a wealth of data; however, use of these data to optimize glycemic control remains limited. We evaluated the impact of a CGM-based decision support system (DSS) in patients with T1D using multiple daily injections (MDI). The studied DSS included real-time dosing advice and retrospective therapy optimization. Adults and adolescents (age >15 years) with T1D using MDI were enrolled at three sites in a 14-week randomized controlled trial of MDI + CGM + DSS versus MDI + CGM. All participants (N = 80) used degludec basal insulin and Dexcom G5 CGM. CGM-based and patient-reported outcomes were analyzed. Within the DSS group, ad hoc analysis further contrasted active versus nonactive DSS users. No significant differences were detected between experimental and control groups (e.g., time in range [TIR] +3.3% with CGM vs. +4.4% with DSS). Participants in both groups reported lower HbA1c (-0.3%; P = 0.001) with respect to baseline. While TIR may have improved in both groups, it was statistically significant only for DSS; the same was apparent for time spent <60 mg/dL. Active versus nonactive DSS users showed lower risk of and exposure to hypoglycemia with system use. Our DSS seems to be a feasible option for individuals using MDI, although the glycemic benefits associated with use need to be further investigated. System design, therapy requirements, and target population should be further refined prior to use in clinical care.

A study on investigation of the usage of decision support systems and evidence-based medicine relations via machine learning algorithms

A study on investigation of the usage of decision support systems and evidence-based medicine relations via machine learning algorithms

The usage of decision support systems in North Macedonian companies

The usage of decision support systems in North Macedonian companies

The usage of decision support systems for improving managerial decision making process

The usage of decision support systems for improving managerial decision making process

CRITICAL SUCCESS FACTORS IN THE IMPLEMENTATION OF INFORMATION SYSTEMS FROM THE PERCEPTION OF USERS: A PUBLIC SECTOR EXPERIENCE

The objective of the article is to identify the perception of users of the Decision Support System (DSS), used by the Federal University of Brazil (UNIVASF), about the management of the Critical Success Factors (CSF) in the implementation of the Information System. The research, descriptive and quantitative, had data collected through a structured questionnaire, applied to Decision Support System users of the Federal University. The results reveal that organizational factors were perceived as those that received more adequate management, when compared to technological CSFs, while compliance with legislation was the factor that obtained the highest positive perception index. It should also be noted that preventive corrective initiatives adopted by managers were not sufficient, leading to this factor being the one with the least positive perception of adequate management. In addition, the career can affect to a certain extent the level of perception Management of the process of implementation of the information system.

A six-year repeated evaluation of computerized clinical decision support system user acceptability

ObjectiveLong-term acceptability among computerized clinical decision support system (CDSS) users in pediatrics is unknown. We examine user acceptance patterns over six years of our continuous computerized CDSS integration and updates. Materials and methodsUsers of Child Health Improvement through Computer Automation (CHICA), a CDSS integrated into clinical workflows and used in several urban pediatric community clinics, completed annual surveys including 11 questions covering user acceptability. We compared responses across years within a single healthcare system and between two healthcare systems. We used logistic regression to assess the odds of a favorable response to each question by survey year, clinic role, part-time status, and frequency of CHICA use. ResultsData came from 380 completed surveys between 2011 and 2016. Responses were significantly more favorable for all but one measure by 2016 (OR range 2.90–12.17, all p < 0.01). Increasing system maturity was associated with improved perceived function of CHICA (OR range 4.24–7.58, p < 0.03). User familiarity was positively associated with perceived CDSS function (OR range 3.44–8.17, p < 0.05) and usability (OR range 9.71–15.89, p < 0.01) opinions. ConclusionWe present a long-term, repeated follow-up of user acceptability of a CDSS. Favorable opinions of the CDSS were more likely in frequent users, physicians and advanced practitioners, and full-time workers. CHICA acceptability increased as it matured and users become more familiar with it. System quality improvement, user support, and patience are important in achieving wide-ranging, sustainable acceptance of CDSS.

ECG Decision Support System based on feedforward Neural Networks

Abstract The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a correlation between features was applied. After that, networks were configured and consequently used for the ECG classification. Next, with respect to the performance criteria fixed by the DSS users, a comparative study between them was deduced. Results show that for classifying the MIT-BIH arrhythmia database signals, the RBF (ACC = 99.9%) was retained as the most accurate network, the PNN (Tr_ttime = 0.070 s) as the rapidest network in the training stage and the MLP (Test_time = 0.096 s) as the rapidest network in testing stage.

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”

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.

A conceptual model selection of big data application: improvement for decision support system user organisation

This study discusses the components influencing the successful selections of big data decision support application in decision support system user organisations. Conceptual framework depicts the model for successful selections of big data application in DSS user organisations. The paper presents a theoretical framework for usage of decision support systems in organisations in order to help the selection of big data applications by following specific steps.

A conceptual model selection of big data application: improvement for decision support system user organisation

This study discusses the components influencing the successful selections of big data decision support application in decision support system user organisations. Conceptual framework depicts the model for successful selections of big data application in DSS user organisations. The paper presents a theoretical framework for usage of decision support systems in organisations in order to help the selection of big data applications by following specific steps.