Journal articles: 'Group decision making. Decision support systems. Performance'

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Silvi, Riccardo, Monica Bartolini, Anna Raffoni, and Franco Visani. "Business Performance Analytics: level of adoption and support provided to Performance Measurement Systems." MANAGEMENT CONTROL, no. 3 (December 2012): 117–42. http://dx.doi.org/10.3280/maco2013-su3006.

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Despite several studies on the importance of Performance Measurement Systems (PMS), often their implementation is considered insufficient or unsatisfactory. This situation is motivating research on several issues: PMS design and focus, cultural and technological aspects, and the specific contexts of implementation. This paper introduces Business Performance Analytics (BPA) as an important framework for successful PMS design and adoption. Specifically, it defines BPA as the use of data and analytical methods to understand and control business dynamics and performance. Based on an exploratory survey on a group of global companies, the paper highlights the importance of measurement not fully delivered by current PMS, together with a first examination of the implementation of BPA in practice. The analysis shows a low level of adoption of BPA both for strategic and operational decisions. At the same time, companies adopting BPA have a higher perception of relevance of statistical and mathematical tools for supporting the decision-making process. The results also show the presence of the two clusters with very different approaches to performance measurement. The "advanced" one is characterized by the implementation of Business Intelligence tools, together with advanced management accounting and PMS approaches. Compared with the "basic" one, the "advanced" cluster shows a higher perception of BPA effectiveness in supporting the decision-making process and also a higher satisfaction for the contribution provided by the information system to the management action.

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Morrison, Ben W., Mark W. Wiggins, and Natalie M. V. Morrison. "Utility of Expert Cue Exposure as a Mechanism to Improve Decision-Making Performance Among Novice Criminal Investigators." Journal of Cognitive Engineering and Decision Making 12, no. 2 (January 23, 2018): 99–111. http://dx.doi.org/10.1177/1555343417746570.

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The research tested whether systematic exposure to expert-identified cues would improve novice criminal investigators’ cue recognition and, in turn, decision making. Two studies are reported, the first of which was a pre- to postexposure assessment of 20 novices’ cue recognition. This involved testing novices’ recognition (accuracy and latency) of pairings of text-based labels (elicited via cognitive task analyses with subject matter experts) prior to and following an exposure phase. The results revealed statistically significant improvements in comparison with a control group. In the second study, an assessment of 36 novices’ decision-making performance was undertaken prior to and following cue-based exposure (either expert or control cues). Participants engaged one of two decision tasks, which varied in the level of decision support offered: high (i.e., most pertinent features were highlighted for users) or low (i.e., features were naturally “embedded” in the task environment). Although participants receiving expert cue exposure demonstrated improvements in decision-making efficiency, advances in accuracy could be established only where a high level of support was offered. It was concluded that expert cue exposure can offer opportunities for learner development; however, a combination of exposure programs and decision support systems offers the greatest potential in improving the situation assessment skills of less experienced investigators.

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Kruse, Clemens Scott, and Nolan Ehrbar. "Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review." JMIR Medical Informatics 8, no. 8 (August 11, 2020): e17283. http://dx.doi.org/10.2196/17283.

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Background Computerized decision support systems (CDSSs) are software programs that support the decision making of practitioners and other staff. Other reviews have analyzed the relationship between CDSSs, practitioner performance, and patient outcomes. These reviews reported positive practitioner performance in over half the articles analyzed, but very little information was found for patient outcomes. Objective The purpose of this review was to analyze the relationship between CDSSs, practitioner performance, and patient medical outcomes. PubMed, CINAHL, Embase, Web of Science, and Cochrane databases were queried. Methods Articles were chosen based on year published (last 10 years), high quality, peer-reviewed sources, and discussion of the relationship between the use of CDSS as an intervention and links to practitioner performance or patient outcomes. Reviewers used an Excel spreadsheet (Microsoft Corporation) to collect information on the relationship between CDSSs and practitioner performance or patient outcomes. Reviewers also collected observations of participants, intervention, comparison with control group, outcomes, and study design (PICOS) along with those showing implicit bias. Articles were analyzed by multiple reviewers following the Kruse protocol for systematic reviews. Data were organized into multiple tables for analysis and reporting. Results Themes were identified for both practitioner performance (n=38) and medical outcomes (n=36). A total of 66% (25/38) of articles had occurrences of positive practitioner performance, 13% (5/38) found no difference in practitioner performance, and 21% (8/38) did not report or discuss practitioner performance. Zero articles reported negative practitioner performance. A total of 61% (22/36) of articles had occurrences of positive patient medical outcomes, 8% (3/36) found no statistically significant difference in medical outcomes between intervention and control groups, and 31% (11/36) did not report or discuss medical outcomes. Zero articles found negative patient medical outcomes attributed to using CDSSs. Conclusions Results of this review are commensurate with previous reviews with similar objectives, but unlike these reviews we found a high level of reporting of positive effects on patient medical outcomes.

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Forcino, Rachel C., Marcus Thygeson, A. James O’Malley, Marjan J. Meinders, Gert P. Westert, and Glyn Elwyn. "Measuring Patient-Reported Shared Decision-Making to Promote Performance Transparency and Value-Based Payment: Assessment of collaboRATE’s Group-Level Reliability." Journal of Patient Experience 7, no. 5 (November 3, 2019): 742–48. http://dx.doi.org/10.1177/2374373519884835.

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Shared decision-making (SDM) between clinicians and patients is a key component of patient experience, but measurement efforts have been hampered by a lack of valid and reliable measures that are feasible for routine use. In this study, we aim to investigate collaboRATE’s reliability, calculate required sample sizes for reliable measurement, and compare Consumer Assessment of Healthcare Providers and Systems (CAHPS) patient experience survey items to collaboRATE. CollaboRATE’s provider group-level reliability reached acceptable reliability at 190 patient reports, while the CAHPS SDM measure demonstrated similar reliability at a sample size of 124. The CAHPS communication measure reached acceptable reliability with 55 patient reports. A strong correlation was observed between collaboRATE and CAHPS communication measures ( r = 0.83). As a reliable measure of SDM, collaboRATE may be useful for both building payment models that support shared clinical decision-making and encouraging data transparency with regard to provider group performance.

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Salehi, Pouria, Erin K. Chiou, and Adam Wilkins. "Human-Agent Interactions: Does Accountability Matter in Interactive Control Automation?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1643–47. http://dx.doi.org/10.1177/1541931218621373.

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In human-automation systems, where high situation awareness is associated with better decision-making, understanding accountability may be crucial to preventing automation complacency. In supervisory control automation, there is some evidence that accountability increases human-automation performance; however, with increasingly intelligent automated agents, human-agent work relationships may resemble more interactive control compared to supervisory control. We investigate the effects of social accountability in a simulated joint task environment and hypothesize that people under an accountability condition would cooperate more with an automated agent than people under a non-accountability condition, in a shared cognitive task. Results from our study support this hypothesis. However, for the accountability group, people’s performance in terms of units processed was lower, and this group also self-reported lower performance and attentional control, with higher frustration. These findings indicate that accountability may slow the decision-making process through added pressure, with some costs to short term efficiency.

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Sambamurthy, V., and Wynne W. Chin. "The Effects of Group Attitudes Toward Alternative GDSS Designs on the Decision-making Performance of Computer-Supported Groups." Decision Sciences 25, no. 2 (March 1994): 215–41. http://dx.doi.org/10.1111/j.1540-5915.1994.tb01840.x.

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Parra López, Carlos, Javier Calatrava Requena, and Tomás De Haro Giménez. "Evaluación comparativa multifuncional de sistemas agrarios mediante AHP: Aplicación al olivar ecológico, integrado y convencional de Andalucía." Economía Agraria y Recursos Naturales 5, no. 9 (October 19, 2011): 27. http://dx.doi.org/10.7201/earn.2005.09.02.

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Even though multifunctionality concept is reflected, implicit or explicitly, in the design of actual agrarian policies, its consideration when analysing and assessing farming systems is relatively limited in the scientific literature. Analytic Hierarchy Process (AHP) is proposed with this aim. AHP is a multicriteria discrete decision support technique that is used in complex decision making. This methodology is stated jointly with a proposed procedure to measure relative agreement among decision makers and uniformity of alternatives’ performances in group decision making. Finally AHP is implemented in the assessment of organic, integrated and conventional olive groves in Andalusia considering criteria of a different nature – economic, technical, sociocultural and environmental –. The final purpose is determining the more interesting growing techniques from a holistic point of view for all the society in the medium/long-term on the basis of knowledge of experts on olive.

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Parra-López, Carlos, Javier Calatrava-Requena, and Tomás de-Haro-Giménez. "A multi-criteria evaluation of the environmental performances of conventional, organic and integrated olive-growing systems in the south of Spain based on experts' knowledge." Renewable Agriculture and Food Systems 22, no. 3 (August 8, 2007): 189–203. http://dx.doi.org/10.1017/s1742170507001731.

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AbstractThe medium to long-term environmental performances of organic, integrated and conventional olive-growing systems in the average conditions of the south of Spain are evaluated and compared with respect to soil erosion, soil fertility, rational use of irrigation water, water contamination, atmospheric pollution and biodiversity, based on experts’ knowledge. The aim of the research was to test the common implicit assumption of environmental superiority of the two alternative farming systems over the conventional system. For this purpose, the Analytic Hierarchy Process (AHP), a widely used multi-criteria decision-making tool, has been implemented. AHP enables us to deal with complex decision-making problems with multiple criteria, stakeholders and decision-makers, high uncertainty and risk, such as in the case of multi-criteria environmental comparison of alternative farming systems. Twenty experts in olive production, clustered into three groups according to their professional field of interest, were involved in the analysis. The utilization of experts' knowledge is justified when information relevant for urgent decision-making is not available, is partial or is time and resource demanding, and a holistic perspective is required. Indexes and procedures are proposed for group decision-making, to detect variation in expert opinions and differences between alternative systems' performances. Despite bias in the judgments of the groups of experts in some topics, results confirm the holistic environmental superiority of organic and integrated alternatives over the conventional olive system in Andalusia in the medium to long-term. The results represent a scientific base to justify and endorse institutional support regarding the promotion and implementation of organic and integrated olive-growing systems in the region, which are likely to result in greater social welfare.

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Kapovits, Geza, and Gergely Dobszay. "Possibilities and Problems in Applying Decision Support Systems for Special Challenges in the Design of Building Constructions." Advanced Materials Research 899 (February 2014): 583–88. http://dx.doi.org/10.4028/www.scientific.net/amr.899.583.

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The objective of this study is to examine the possibility of using DSS in the engineering design of a special group of building constructions: 'cladded roofs'. These are roofs whose cladding is the same as the façade’s, giving buildings a homogeneous appearance. For architectural impression the roof is made of unusual materials and complex structures thus cannot be designed with traditional guidelines. The goal of using DSS is to facilitate and simplify the range of possible materials and structures, to assist and expedite finding the optimal version and to reduce the likelihood of design flaws. In addition to eco-efficiency in production, transportation, and energy consumption of buildings, we hold it to be important to reduce the amount of materials used to a necessary minimum. [1] To achieve this all materials should be used at places where their specific characteristics prevail most effectively. The criteria for applying DSS for ‘cladded roofs’ is the successful structuring of the necessary constructional data and variations. We propose a method to generate numerous proposed solutions and rank them based on their technical performance and value. The result is a kind of 'guided decision-making' which may be an important aid in the design of nonstandard structures.

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Wulf, Anna, and Lynne Butel. "Knowledge sharing and collaborative relationships in business ecosystems and networks." Industrial Management & Data Systems 117, no. 7 (August 14, 2017): 1407–25. http://dx.doi.org/10.1108/imds-09-2016-0408.

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Purpose The sharing of knowledge between partners in collaborative relationships is widely accepted to be fundamental to supporting strategic decision making, particularly in relation to innovation management and business sustainability. The purpose of this paper is to focus on how the structure of collaborative relationships in business networks may determine successful knowledge sharing and thus improve decision making and business performance. Design/methodology/approach Expert interviews were conducted with participants operating in networks and business ecosystem in four different sectors in Italy and Germany, exploring the process of knowledge sharing, organisational learning and decision making within collaborative relationships. A qualitative textual analysis was used to analyse the experts’ responses. Findings The research found that an organisation’s network position and the network structure, as well as the governance and richness of the business ecosystem in which it operates, influence its ability to share knowledge, to innovate and therefore to compete sustainably. Research limitations/implications The research demonstrates that innovative strategic decision making, based on access to appropriate knowledge, occurs within the context of social and business network relations operating within a broader more diverse business ecosystem. Closer dyadic or small working group ties best facilitate trust and sharing of the most valuable knowledge. Appropriate participation in and management of such structures is therefore essential to support knowledge-based decision making, and critical to sustained competitive advantage. Originality/value The research focusses on how interfirm relationships are established and maintained, how firms establish trust and facilitate knowledge sharing forming the basis of organisational learning.

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Bahadori, Mohammad Sadegh, Alexandre B. Gonçalves, and Filipe Moura. "A Systematic Review of Station Location Techniques for Bicycle-Sharing Systems Planning and Operation." ISPRS International Journal of Geo-Information 10, no. 8 (August 17, 2021): 554. http://dx.doi.org/10.3390/ijgi10080554.

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Designing or expanding a bicycle-sharing system (BSS) involves addressing the infrastructure’s location of the bicycle stations. Station location is an essential factor for designing and implementing a new system or for its operation. In a complex spatial optimization context, geographic information systems (GIS) can support this decision problem. There are also numerous ways of subdividing the broad spectrum of location-allocation models used in previous studies. However, a station location comprehensive review and systematization with the specific aim of characterizing the state of the art of BSS is missing. The present research aimed to provide a comprehensive systematization for station location problems, criteria, and techniques, seeking to identify the current state of practice. We searched scientific publication databases to collect relevant publications—the final list comprised 24 papers for the literature review. The systematization addresses the two major problems concerning bicycle station location: initial network design and operation improvement (where changes in operating a BSS are implemented). Based on the literature, we propose a set of four main criteria for choosing appropriate places for bike stations (or parking) in a city: “bike network”, “operator”, “user”, and “city infrastructure”. The sub-criteria mentioned in the literature are categorized based on the proposed classification and new sub-criteria are suggested. We also group location modeling techniques into three categories: “mathematical algorithms”, “multi-criteria decision making”, and “GIS”. Combining GIS and multi-criteria decision making (MCDM) has received more attention in recent years to locate bike stations, evaluate their operating performance, and have more accurate and practical results.

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K, Rahimah, A. H. Nor Aziati, Adnan H.B, Helmi Adly Mohd Noor, and Fauziah Abdul Rahman. "Organization Support for Cloud Computing Implementation Success in Education System: Scale Development and Validity in Delphi." International Journal of Engineering & Technology 7, no. 3.30 (August 24, 2018): 512. http://dx.doi.org/10.14419/ijet.v7i3.30.18420.

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Cloud computing (CC) support for learning systems has been viewed as one of the most discussed issues that promise to modernize computing by providing visualized resources as a service over the internet. To be stable in cloud computing acquisition requires an education institution to address many of the same concerns they face in implementing an Information System (IS) service. Currently, there is still lack of CC implementation standard and organizational support that impacted VLE system performance. Previous research has reported that the influence of the CC implementation decision depends on the impact of various factors studied. Nonetheless, organizational support is the least factor mentioned especially studies from Malaysia. Thus, the main purpose of this study is to develop a validated scale of organizational support in implementation decision activities towards CC implementation success. In this paper, the Delphi process adopted to measure consensus among nominal group technique (also known as the expert panel). Key methodological issues in using the methods are discussed, along with the distinct contribution of consensus methods as aids to decision making in education service development. The study has adapted stages of proses flow of scale development and validation of measurement items according to legitimate measures in the Delphi technique. The measurement scales formed are based on literature review and field studies conducted to increase the reliability and validity values. Organizational support constructs were divided into top management support, firm size, awareness, Technology Readiness and cost effectiveness. A total of 5 items have been successfully set up for further validation.

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Gilbert, Natalia, Rodrigo Assar, and Rodrigo Martinez. "P.1.11 ‘Creation of classification model using machine learning; to detect dysphonia work-related’." Occupational and Environmental Medicine 76, Suppl 1 (April 2019): A80.2—A80. http://dx.doi.org/10.1136/oem-2019-epi.216.

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The underdiagnosis of occupational disease causes severe damage to the health system. The classification of a disease as a professional is based on the decision on whether the present labor factors are sufficient for the generation of the disease, and this function is carried out by a qualified professional or committee.Occupational dysphonia is one of the 5 most frequent occupational diseases in Chile, whose condition impact on the labor productivity and the quality of life of the patient. Today there are no unified criteria among the occupational qualification decisión makers to decide on the sufficient of laboral factors of occupational dysphonia disease.Computerized systems have been developed to support clinical diagnosis decision-making process; among these, Machine Learning methods have been used to simulate the reasoning of the expert from the analysis and identification of complex patterns in large databases, so in this study it is suggested that the creation of a dysphonia classification model is possible employing Machine Learning tools. For this purpose, 103 cases obtained from patients with qualification results cause by dysphonia was analize in relation to the number of variables studied and their distribution for the observation of the characteristics that give identity to the groups studied. Subsequently, different classification models were developed using Machine Learning and the one that presented the best performance was chosen.Statistical analyzes show that of the 6 models of Machine Learning elaborated, Random Forest was the one that presented the best performance (accuracity=0.83 and Kappa value=0.61), variables that manage to establish identity to each group represent 26.5% of the total of studied variables. The results in this work show the potential of the use of computer tools can be useful as a support tool for diagnosis of occupational disease.

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Arjan, Khushal, Lui G. Forni, Richard M. Venn, David Hunt, and Luke Eliot Hodgson. "Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA." PLOS ONE 16, no. 3 (March 18, 2021): e0248477. http://dx.doi.org/10.1371/journal.pone.0248477.

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Objectives of the study Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making. Methods Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay. Results Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78–0.80), superior to NEWS2 0.65 (0.62–0.67) and CFS 0.76 (0.74–0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8–16.1) to stay >30 days. Conclusions Our simple validated model (Older Persons’ Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.

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Vankayalapati, Revathi, Kalyani Balaso Ghutugade, Rekha Vannapuram, and Bejjanki Pooja Sree Prasanna. "K-Means Algorithm for Clustering of Learners Performance Levels Using Machine Learning Techniques." Revue d'Intelligence Artificielle 35, no. 1 (February 28, 2021): 99–104. http://dx.doi.org/10.18280/ria.350112.

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Data Clustering is the process of grouping the objects in a way which is identical to the objects in the same group than in other classes. In this paper, the clustering of data is used as k-means to assess the output of students. Machine Learning is an area used in all systems. Machine learning is used in education, pattern recognition, sports, industrial applications. Its significance increases with the future of the students in the educational system. Data collection in education is very useful, as data volumes in the education system are growing each day. Higher education is relatively new, but due to the growing database its significance grows. There are several ways to assess the success of students. K-means is one of the best and most successful methods. The secret information in the database is extracted using data mining to increase the output of students. The decision tree is also a way to predict the success of the students. In recent years, educational institutions have the greatest challenges in increasing data growth and using it to increase efficiency, such that better decision-making can be made. Clustering is one of the most important methods used for the analysis of data sets. This trial uses cluster analyses according to their features for section students in various classes. Uncontrolled K-means algorithm is discussed. The mining of education data is used for the study of the knowledge available in the field of education in order to provide secret, significant and useful information. The proposed model considers K-means clustering model for analyzing learners performance. The outcomes and future of students can be strengthened with this support. The results show that the K-means cluster algorithm is useful for grouping students based on similar performance features.

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McGrath, Michael Robert. "Strategic decision making and group decision support systems." New Directions for Institutional Research 1986, no. 49 (1986): 65–73. http://dx.doi.org/10.1002/ir.37019864907.

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Bin Rodzman, Shaiful Bakhtiar, Nordin Abu Bakar, Yun-Huoy Choo, Syed Ahmad Aljunid, Normaly Kamal Ismail, Nurazzah Abd Rahman, and Marshima Mohd Rosli. "I-OnAR: a rule-based machine learning approach for intelligent assessment in an online learning environment." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (February 1, 2020): 1021. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp1021-1028.

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Intelligent systems are created to automate decision making process that is similar to human intelligence. Incorporating intelligent component has achieved promising results in many applications, including in education. Intelligence modules in a tutoring system would bring the application and its capability closer to a human's ability to serve its human users and to solve problems. However, the majority of the online learning provided in the literature review especially in Malaysia, normally only provide the lecture notes, assignments and tests and rarely suggest or give feedbacks on what the students should study or do next in order to fully understand the subjects. Hence, the researchers propose an online learning environment called Intelligent Online Assessment and Revision (I-OnAR). It facilitates the learning process at multiple learning phases such as test creation, materials revision, feedback for improvement and performance analysis. These components are incorporated into the tutoring system to assist self-pace learning at anytime and anywhere. The intelligent agent uses a Rule-based Machine Learning method for the adaptive capabilities such as automated test creation and feedbacks for improvement. The system has been tested on a group of students and found to be useful to support learning process. The results have shown that 60% of the subjects’ performance have improved with the help of the system. The students were given feedbacks on the topic they did poorly as well as how to improve their performance. This proves that the Intelligent Online Assessment and revision (I-OnAR) can be a useful tool to help online students intelligently, systematically and efficiently. For the future works, the researchers would like to apply the usage of other techniques such as Fuzzy Logic to strengthen the analysis and decision of the current system.

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Bowen, Natasha K., Robert Lucio, Michele Patak-Pietrafesa, and Gary L. Bowen. "The SSP 2020: The Revised School Success Profile." Children & Schools 42, no. 1 (January 2020): 19–28. http://dx.doi.org/10.1093/cs/cdz025.

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Abstract To support student success effectively, school teams need information on known predictors of youth behavior and academic performance. In contrast to measures of behavioral and academic outcomes that are commonly relied on in schools, the School Success Profile (SSP) for middle and high school students provides comprehensive information on predictors of outcomes that reside in students’ neighborhoods, schools, peer systems, and families. This article presents the SSP 2020, a newly revised version of the SSP that is now freely available to schools and researchers. The online, self-report SSP 2020 retains the strengths of the SSP but is shorter and simpler. Revised group- and individual-level reports are automatically generated once SSP data are collected. The SSP 2020 dashboard allows users to request reports on numerous demographic subgroups. A comprehensive prevention road map walks teams through each step of an evidence-informed decision-making process based on SSP 2020 data. Resources embedded in the road map include information on evidence-informed prevention strategies for SSP dimensions with corresponding brief assessments and fidelity checklists. School social workers are encouraged to take the lead in bringing the SSP 2020 to school teams charged with promoting student behavioral and academic success.

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Suresh, S. "Stress and Coping Strategies." Management and Labour Studies 33, no. 4 (November 2008): 482–87. http://dx.doi.org/10.1177/0258042x0803300403.

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Stress is believed to be a state of the mind as well as the body, created by certain biochemical reactions in the human body as well as psychological responses to situations, and is reflected by a sense of anxiety, tension and depression and is caused by such demands by the environmental forces or internal forces that cannot be met by the resources available to the person. The greater the gap between the demands and the resources, the greater is the degree of stress. Some of the individual strategies for coping with stress include: readjustment of life goals, support from family and friends, planning certain events of life in advance and keeping the body in good physiological shape by proper diet, exercise, yoga, meditation and biofeedback. Some of the organizational strategies for coping with stress include organized health maintenance facilities as a part of the organizational life, matching of employees qualifications with job requirements, job enrichment and job work redesigns, equitable performance appraisal and reward systems, participation in organizational decision making and building team spirit in the sense that there should be no interpersonal conflict within the group. All these strategies or a combination thereof should be applied to make the work environment less stressful to a level which is positive and challenging.

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Vanhaeren, Nina, Kristien Ooms, and Philippe De Maeyer. "The development of a cognitive indoor route planning algorithm: which aspects to include?" Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-378-2019.

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Abstract. Wayfinding in the indoor environment is challenging and people often get lost indoors. Navigation systems guide people in these environments, however, the existing systems are not well adapted to their users. The focus of our research is on the route planning aspect of navigation systems. By adapting the routes that people are guided along in the environment, our goal is to substantially improve the wayfinding experience for the users of those systems. Guiding people along routes that adhere better to their cognitive processes could ease the wayfinder in the indoor environment.To select the aspects that should be implemented in a routing algorithm that calculates such routes, a mixed method approach was applied. In this approach, the results of an exploratory focus group and a complementary online survey were combined. To validate these results, a real-life experiment is being developed at the time of writing.Our first study, the exploratory focus group, comprised academic researchers and experts with different background (i.e. Psychology, Geography and Architectural Design). The discussions were guided by a rotating wheel according to the GPS-method which was developed by the Flanders District of Creativity. These discussions provided a broad overview of the elements to be regarded when studying wayfinding. Moreover, results indicate that route complexity has to be considered at different levels: local level (i.e. at decision points) and global level (i.e. legibility of the building). Based on the results of the focus group, multiple situations with specific local characteristics likely inducing confusion and discomfort (e.g. specific intersections, different stair cases, different door types) were selected to elaborate more deeply in the complementary online survey. In this survey, videos of these situations were shown to the participants, as if they were navigating through the building. They were asked to rank their comfort and confusion level about the recorded situation on a 5-point Likert-scale. The results show that visibility, visual clutter and geometric simplicity are of substantial importance when evaluating comfort and confusion levels, and thus the complexity of indoor navigation situations.Since body-movement and the real-world perceptions, which have a substantial impact on information processing and spatial decision making [1], are excluded in these well-controlled lab environments of the previous studies, a real-life experiment will be executed to validate previous findings. The developed study design is in line with the experiment design of previous wayfinding studies [2], [3]. Eye tracking data of participants guided through different complex buildings along different paths (i.e. shortest path and fewest turn path) will be recorded. Performance measures (e.g. duration, stops, errors), eye tracking measures (e.g. fixation number, fixating duration) and annotations of the accompanying researcher, which are all measures indicating complexity and cognitive load, will be compared across the different paths and its decision points. This analysis will allow us to determine complex routes and to identify the environmental characteristics increasing the perceived complexity. Moreover, it will lead to an understanding how and in which occasions people make wayfinding errors.In a subsequent phase of our research project, these results will be incorporated in a cognitively-sounding route planning algorithm which could be a valuable improvement of indoor navigation support. Focus will be on the theoretical interpretation of the underlying spatial concepts. Adjusting the route planning support of indoor navigation systems to human wayfinding behavior could be a substantial contribution in this area.

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Lewandowski, Andrzej. "SCDAS — Decision support system for group decision making: Decision theoretic framework." Decision Support Systems 5, no. 4 (December 1989): 403–23. http://dx.doi.org/10.1016/0167-9236(89)90019-5.

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Postmes, Tom, and Martin Lea. "Social processes and group decision making: anonymity in group decision support systems." Ergonomics 43, no. 8 (August 2000): 1252–74. http://dx.doi.org/10.1080/00140130050084978.

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Asher, Anthony L., Paul C. McCormick, Nathan R. Selden, Zoher Ghogawala, and Matthew J. McGirt. "The National Neurosurgery Quality and Outcomes Database and NeuroPoint Alliance: rationale, development, and implementation." Neurosurgical Focus 34, no. 1 (January 2013): E2. http://dx.doi.org/10.3171/2012.10.focus12311.

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Patient care data will soon inform all areas of health care decision making and will define clinical performance. Organized neurosurgery believes that prospective, systematic tracking of practice patterns and patient outcomes will allow neurosurgeons to improve the quality and efficiency and, ultimately, the value of care. In support of this mission, the American Association of Neurological Surgeons, in cooperation with a broad coalition of other neurosurgical societies including the Congress of Neurological Surgeons, Society of Neurological Surgeons, and American Board of Neurological Surgery, created the NeuroPoint Alliance (NPA), a not-for-profit corporation, in 2008. The NPA coordinates a variety of national projects involving the acquisition, analysis, and reporting of clinical data from neurosurgical practice using online technologies. It was designed to meet the health care quality and related research needs of individual neurosurgeons and neurosurgical practices, national organizations, health care plans, biomedical industry, and government agencies. To meet the growing need for tools to measure and promote high-quality care, NPA collaborated with several national stakeholders to create an unprecedented program: the National Neurosurgery Quality and Outcomes Database (N2QOD). This resource will allow any US neurosurgeon, practice group, or hospital system to contribute to and access aggregate quality and outcomes data through a centralized, nationally coordinated clinical registry. This paper describes the practical and scientific justifications for a national neurosurgical registry; the conceptualization, design, development, and implementation of the N2QOD; and the likely role of prospective, cooperative clinical data collection systems in evolving systems of neurosurgical training, continuing education, research, public reporting, and maintenance of certification.

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Rao, G. R., and M. Turoff. "A hypermedia-based group decision support system to support collaborative medical decision-making." Decision Support Systems 30, no. 2 (December 2000): 187–216. http://dx.doi.org/10.1016/s0167-9236(00)00096-8.

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Pollock, Clare, and Andrew Kanachowski. "Application of theories of decision making to group decision support systems (GDSS)." International Journal of Human-Computer Interaction 5, no. 1 (January 1993): 71–94. http://dx.doi.org/10.1080/10447319309526056.

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Chen, Shyi-Ming, Li-Wei Lee, Szu-Wei Yang, and Tian-Wei Sheu. "Adaptive consensus support model for group decision making systems." Expert Systems with Applications 39, no. 16 (November 2012): 12580–88. http://dx.doi.org/10.1016/j.eswa.2012.05.026.

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Moon, Jukrin, S. Camille Peres, and Farzan Sasangohar. "Defining Team Cognition in Emergency Response: A Scoping Literature Review." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 894–95. http://dx.doi.org/10.1177/1541931213601702.

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With so many natural and man-made disasters bringing catastrophic losses worldwide, the initial response is the one during which real-time decision-making is particularly important for the overall performance. During the response phase, first responders with diverse backgrounds (e.g., fire, medical, law enforcement, or public work) need to work together as an ad hoc team to save lives and infrastructures at risk. For successful response operations, cognition (i.e., the ability to support timely and effective decision-making) needs to be understood not only at the individual level but also at the team level. Yet, team cognition still remains to be an inadequately addressed topic in emergency response literature (Bigley & Roberts, 2001; Comfort, 2007; Majchrzak, Jarvenpaa, & Hollingshead, 2007). While the need to study real-world teams in the context of “broader sociotechnical systems” has been documented in team cognition and behavior literature (Kozlowski & Ilgen, 2006; Salas, Cooke & Rosen, 2008; DeChurch and Zaccaro, 2010), emergency response literature lacks attention to emergency responders’ collective efforts. Unlike many other cognitive psychology research topics, the theoretical growth of team cognition has been primarily driven by application needs – i.e., “there was no time to wait for a psychology of team cognition” because applications were “needed yesterday” (Cooke et al. 2007). That is, the scarcity of literature can be traced back to lack of efforts in conceptualizing and operationalizing team cognition in the unique context of emergency response. Most of all, the emergency response field must first come to consensus on what it means by team cognition. As it stands, there are at least five research domains of team cognition (Wildman, Salas, & Scott, 2014), i.e., team mental models, transactive memory systems, team situation awareness, strategic consensus, and interactive team cognition (ITC). While each of these domains highlights some aspects of the multifaceted construct of team cognition, the direction of future research hinges on having a clear conceptualization of team cognition – particularly in the context of emergency response. A scoping review of literature was conducted as an initial effort to outline and synthesize how team cognition has been defined in the field of emergency response. Using targeted keyword searches in MEDLINE, COMPENDEX and CINAHL, the total of 1,799 articles published in English after 1994, which either empirically or otherwise investigated team cognition in emergency response were retrieved. After the initial search, paper titles, abstracts, and full texts were subsequently reviewed to exclude irrelevant ones and to define the subset for detailed review. Our qualitative analysis identified inconsistencies in definitions that may generate redundant research efforts and hinder the generalization of findings. Three main research gaps with respect to defining team cognition in emergency response are identified. (1) Team cognition needs to be defined at the team level, viewing a team either as an aggregated group of individuals or a cognitive system. (2) Product-based and process-based definitions of team cognition need to be clearly differentiated and aligned with two distinct views on a team. While team cognition has a nuanced literature that describes subtle distinctions between two perspectives (i.e., an emergent state vs. a cognitive process) (Durso, Rawson, & Girotto, 2007; Cooke et al. 2007; Saner et al. 2009), emergency response literature has yet to produce research that shows satisfactory exploration on those distinctions. (3) Team cognition needs to be redefined in the unique context of emergency response. As posited by the ITC theory (“team cognition is inextricably tied to context”, Cooke et al. 2013), team cognition in emergency response could differ depending on the nature of the surrounding dynamic context including team, task, and environment. However, different constructs are taken directly from other disciplines and only very few of them are redefined in the context of emergency response (Sætrevik, 2015). This research documents the first component of the multistage on-going project that investigates how interactions among human, team, and technology affect team cognition and performance in emergency response. Based on the synthesis, a working definition for future work is presented as: “a collective cognitive process of team members 1) perceiving changes in the status of critical elements, 2) adapting in response to the perceived changes, and 3) learning from past performance, which supports timely and effective coordinated decision-making and manifests itself as behavioral patterns of dynamic interactions among individuals and technologies”. Future work in progress includes developing an appropriate measurement technique for team cognition based on working definition, designing and evaluating potential technologies to support team cognition in the simulated emergency response environment (i.e., Emergency Operations Training Center, TEEX, College Station, TX).

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Carneiro, João, Patrícia Alves, Goreti Marreiros, and Paulo Novais. "Group decision support systems for current times: Overcoming the challenges of dispersed group decision-making." Neurocomputing 423 (January 2021): 735–46. http://dx.doi.org/10.1016/j.neucom.2020.04.100.

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Rathwell, Margaret A., and Alan Burns. "Information Systems Support for Group Planning and Decision-Making Activities." MIS Quarterly 9, no. 3 (September 1985): 255. http://dx.doi.org/10.2307/248952.

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Barnett, Barbara J. "Perspectives on Decision Analysis for Decision Support System Design." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 16 (October 1993): 1101. http://dx.doi.org/10.1177/154193129303701613.

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This symposium addresses the characterization of human decision making within a complex environment for the purpose of developing improved decision support systems. All of the work presented in this symposium was conducted under a Navy research program entitled “Tactical Decision Making Under Stress” (TADMUS). The overall objective of the TADMUS program is to improve tactical decision making of anti-air warfare (AAW) crew members within the Aegis cruiser's combat information center (CIC) under conditions of stress and uncertainty. The unique aspect of this effort is that each presentation addresses decision making behavior, within a single domain, from a different perspective. The goal of each effort is to characterize some aspect of expert decision making performance within the AAW task environment, and to make recommendations for the resulting decision support system design based upon these characterizations. The result is a multi-faceted, human-centered approach to information organization and interface display design for a decision support system.

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Daily, Bonnie, Art Whatley, Steven R. Ash, and Robert L. Steiner. "The effects of a group decision support system on culturally diverse and culturally homogeneous group decision making." Information & Management 30, no. 6 (September 1996): 281–89. http://dx.doi.org/10.1016/s0378-7206(96)01062-2.

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Carneiro, João, Diogo Martinho, Patrícia Alves, Luís Conceição, Goreti Marreiros, and Paulo Novais. "A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts." Applied Sciences 10, no. 13 (July 3, 2020): 4614. http://dx.doi.org/10.3390/app10134614.

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To support Group Decision-Making processes when participants are dispersed is a complex task. The biggest challenges are related to communication limitations that impede decision-makers to take advantage of the benefits associated with face-to-face Group Decision-Making processes. Several approaches that intend to aid dispersed groups attaining decisions have been applied to Group Decision Support Systems. However, strategies to support decision-makers in reasoning, understanding the reasons behind the different recommendations, and promoting the decision quality are very limited. In this work, we propose a Multiple Criteria Decision Analysis Framework that intends to overcome those limitations through a set of functionalities that can be used to support decision-makers attaining more informed, consistent, and satisfactory decisions. These functionalities are exposed through a microservice, which is part of a Consensus-Based Group Decision Support System and is used by autonomous software agents to support decision-makers according to their specific needs/interests. We concluded that the proposed framework greatly facilitates the definition of important procedures, allowing decision-makers to take advantage of deciding as a group and to understand the reasons behind the different recommendations and proposals.

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Zhang, Zhiming, and Chong Wu. "A decision support model for group decision making with hesitant multiplicative preference relations." Information Sciences 282 (October 2014): 136–66. http://dx.doi.org/10.1016/j.ins.2014.05.057.

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Kumar Kar, Arpan, and Ashis Kumar Pani. "How can a group of procurement experts select suppliers? An approach for group decision support." Journal of Enterprise Information Management 27, no. 4 (July 8, 2014): 337–57. http://dx.doi.org/10.1108/jeim-10-2012-0076.

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Purpose – The application of theories on group decision support is yet to be explored extensively in supplier selection literature, although the literature in both domains is extremely rich, in isolation. The purpose of this paper is to explore the application of group decision support theories for supplier selection. Design/methodology/approach – The row geometric mean method (RGMM) of the analytic hierarchy process (AHP) has been used in this study for the prioritization of group preferences under consensus. A case study was conducted to test the theories of consensual group decision making and compare it with other approaches based on AHP. Findings – The study establishes that the application of decision support theories for group decision making can improve the supplier selection process. Findings further imply that RGMM is more effective than eigen value method, for group decision making under consensus. Research limitations/implications – Methodologically, the study highlights the greater regularity in outcome of group decision making, vis-à-vis individual decision making, for the same decision-making context. Also, it highlights how RGMM is more effective since it preserves reciprocal properties and diversity in preferences better. Practical implications – The study establishes that firms can improve supplier selection processes by leveraging on the collective expertise of a group rather than depending on individual decision-making expertise. Originality/value – This study explores the application of different theories based on AHP for consensual group decision making. It compares different approaches based on AHP and establishes that RGMM is a superior approach for supplier selection.

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Ziegler, Leopold A., and Melvin F. Shakun. "Evolutionary Systems Design: Policy Making under Complexity and Group Decision Support Systems." Journal of the Operational Research Society 40, no. 11 (November 1989): 1053. http://dx.doi.org/10.2307/2583142.

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Ziegler, Leopold A. "Evolutionary Systems Design: Policy Making Under Complexity and Group Decision Support Systems." Journal of the Operational Research Society 40, no. 11 (November 1989): 1053–54. http://dx.doi.org/10.1057/jors.1989.179.

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Breuil, D. "Evolutionary systems design: Policy making under complexity and group decision support systems." European Journal of Operational Research 37, no. 2 (November 1988): 294. http://dx.doi.org/10.1016/0377-2217(88)90343-8.

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BUI, CONG CUONG. "ON GROUP DECISION MAKING UNDER LINGUISTIC ASSESSMENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (August 1999): 301–8. http://dx.doi.org/10.1142/s0218488599000258.

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In this paper we consider a fuzzy logic-based model in group decision making, with a focus on the set of all alternatives and on the individual lingustic preference relations. Some choice processes are devoted to the model using consensus measures and linguistic ordered weighted averaging (LOWA) operator. A multiple criteria group decision model in linguistic setting and some aggregation processes are also considered. The model and the new processes allow to incorporate human consistency in decision support systems.

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Gaigole, Mayur, and Nilay Khere. "Web-Base Group Decision Support System for Information Security Decision Making in Case of Indian E-Government Systems." Advanced Materials Research 403-408 (November 2011): 954–62. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.954.

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This paper presents a web base multicriteria group decision support system for evaluating information security policy decision making with respect to Indian e-government system. This study first proposes a rational-political group decision-making model which identifies three uncertain factors involved in a group decision-making process: decision maker’s roles in a group reaching a satisfactory solution, preferences for alternatives and judgments for assessment criteria. Based on the model, a linguistic term oriented multi criteria group decision-making method is developed. The method uses general fuzzy number to deal with the three uncertain factors described by linguistic terms and aggregates these factors into a group satisfactory decision that is in a most acceptable degree of the group. Moreover, this study implements the method by developing a web-based group decision support system. This system allows decision makers to participate a group decision-making through the web, and manages the group decision-making process as a whole, from criteria generation, alternative evaluation, opinions interaction to decision aggregation. An information security decision making problem is presented to demonstrate the effectiveness of the approach.

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Lee, Hyun-Ku, and Hangjung Zo. "Assimilation of military group decision support systems in Korea." Information Development 33, no. 1 (July 9, 2016): 14–28. http://dx.doi.org/10.1177/0266666916628316.

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The successful assimilation of group decision support systems (GDSS) in the Command, Control, Communications, Computers, and Intelligence (C4I) systems of military organizations is critical for making fast and accurate modern military operations decisions. This study empirically examines factors affecting military group decision support systems’ (MGDSS) assimilation and the mediating effect of structural appropriation in the Korean Army in a technology-organization-environment (TOE) framework. Two hundred and forty-eight users or former users of the Army Tactical Command Information System (ATCIS) participated in the survey and a partial least squares (PLS) analysis was used to analyze the data. The results found that institutional pressure (IP) has the strongest effect on structural appropriation, followed by IT infrastructure (ITI) and top management support (TMS). Structural appropriation plays a significant mediating part between the TOE factors and MGDSS assimilation. The results provide implications and contribute valuable insights for military organizations’ successful assimilation of MGDSS.

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Hamdani, Hamdani, Retantyo Wardoyo, and Khabib Mustofa. "Weighting Model for Group Decision Support System: A Review." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 962. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp962-974.

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This paper was conducted a survey of research findings related to decision-making in groups using weighting techniques This paper conducts a survey of research findings related to decision-making in groups using weighting techniques, therefore, a new weighting model can be proposed.. In order to make a decision, this model works based on the weighting parameters, criteria or decision makers (DM) to give ranking preference in the decision results. Weighting could be done objectively or subjectively by statistical calculations. Subjective weighting based on an understanding or expertise of the decision-making process, furthermore, a numerical value was needed to make it objective. The previous weighting models need to be modified for the development of group decision support systems (GDSS). It is required to accommodate the interests of all stakeholders to link the DM relationship. We formed several groups of weighting methods that currently use for the trend of group decision-making (GDM). It showed that the classical multi-criteria decision-making (MCDM) models are still dominant in solving GDSS problems, therefore, it was necessary to apply hybrid MCDM with an approach method of stakeholders models and social networks (SN) to improve decision model that has been applied in the previous research.

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Godé, Cécile, and Jean-Fabrice Lebraty. "Improving Decision Making in Extreme Situations." International Journal of Technology and Human Interaction 9, no. 1 (January 2013): 1–17. http://dx.doi.org/10.4018/jthi.2013010101.

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This article explores the capacity of a decision support system (DSS) to improve the quality of decision making in extreme situations. This DSS is actually based on a networked information system (IS). Academic literature commonly mentions models of fit to explore the relationship between technology and performance, reckoning users’ evaluations as a relevant measurement technique for Information System (IS) success. Although effective contributions have been achieved in measurement and exploration of fit, there have been few attempts to investigate the triangulation of fit between “Task-DSS-Decision Maker” under stressful and uncertain events. This article provides new insights regarding the assessment of advantages provided by networked IS for making relevant decisions under such circumstances. An original case study has been conducted. It is focused on a decision support system called Link 16 that is used during aerial missions. This case study shows that the system improves decision making on an individual basis and suggests to take into account three main criteria – compliance, complementarity and conformity – to measure DSS performance under extreme situations.

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Moundalexis, Megan L., and Barin N. Nag. "Decision Making, Dashboard Displays, and Human Performance in Service Systems." International Journal of Information Systems in the Service Sector 5, no. 4 (October 2013): 32–46. http://dx.doi.org/10.4018/ijisss.2013100103.

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Service systems are often characterized by large components of human work and the need to make decisions based on human performance. Human cognitive limitations and the abilities of computers to compensate led to decision support systems (DSS). While a computerized DSS fits the needs of human cognitive limits, the strengths of human cognitive abilities are often overlooked. Human performance is often monitored by task completion in terms of timeliness and accuracy. A failure of this is that cognitive feedback is generally not given to the operator until after the task. Dashboard displays that are already widely used in manufacturing and other operational applications give current performance information and can take advantage of human cognitive capabilities. This paper presents the concept of decision support in human performance by exploring the extension of the dashboard display concept to human performance monitoring as a cognitive feedback mechanism. Examples specific to the service sector are provided in the context of a Help Desk environment.

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Liu, Shaofeng, Alex H. B. Duffy, Robert Ian Whitfield, Iain M. Boyle, and Iain McKenna. "Towards the Realization of an Integrated Decision Support Environment for Organizational Decision Making." International Journal of Decision Support System Technology 1, no. 4 (October 2009): 38–58. http://dx.doi.org/10.4018/jdsst.2009062603.

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Traditional decision support systems are based on the paradigm of a single decision maker working at a standalone computer or terminal who has a specific decision to make with a specific goal in mind. Organizational decision support systems aim to support decision makers at all levels of an organization (from executive, middle management managers to operators), who have a variety of decisions to make, with different priorities, often in a distributed and dynamic environment. Such systems need to be designed and developed with extra functionality to meet the challenges such as collaborative working. This article proposes an Integrated Decision Support Environment (IDSE) for organizational decision making. The IDSE distinguishes itself from traditional decision support systems in that it can flexibly configure and re-configure its functions to support various decision applications. IDSE is an open software platform which allows its users to define their own decision processes and choose their own exiting decision tools to be integrated into the platform. The IDSE is designed and developed based on distributed client/server networking, with a multi-tier integration framework for consistent information exchange and sharing, seamless process co-ordination and synchronisation, and quick access to packaged and legacy systems. The prototype of the IDSE demonstrates good performance in agile response to fast changing decision situations.

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Janeth Lancheros-Cuesta, Diana, Angela Carrillo-Ramos, and Jaime A. Pavlich-Mariscal. "Content adaptation for students with learning difficulties: design and case study." International Journal of Web Information Systems 10, no. 2 (June 10, 2014): 106–30. http://dx.doi.org/10.1108/ijwis-12-2013-0040.

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Purpose – This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic tools are very useful to enhance the learning process in the classroom. The large variety of these tools require advanced decision-making techniques to select parameters, such as student profiles and preferences, to adjust content and information display, according to specific characteristics and necessities of students. They are part of the Kamachiy–Idukay (KI), a platform to offer adaptative educational services to students with learning difficulties or disabilities. Design and Methodology – The design and implementation of the adaptation algorithm comprises the following phases: utilization of the AHP to determine the most important student parameters, parameter to take into account in the adaptation process, such as preferences, learning styles, performance in language, attention and memory aspects and disabilities; designing the first part of the adaptation algorithm, based on a rule-based system; designing the second part of the adaptation algorithm, based on k-means clustering; integration of the adaptation algorithm to KI; and validation of the approach in a primary school in Bogotá (Colombia). Approach – The main approach is the application of computational techniques, namely, rule-based systems and k-means clustering, plus an AHP prioritization at design time to yield a system to support the teaching–learning process for students with disabilities or learning difficulties. Findings – The algorithm found several groups of students with specific learning difficulties that required adapted activities. The algorithm also prioritized activities according to learning style and preferences. The results of the application of this system in a real classroom yielded positive results. Limitations of the research – The algorithm performs adaptation for students with mild disabilities or learning difficulties (language, attention and memory). The algorithm does not address severe disabilities that could greatly affect cognitive abilities. Contributions – The main contribution of this paper is an adaptation algorithm with the following distinctive characteristics, namely, designed utilizing the AHP, which ensures a proper prioritization of the student characteristics in the adaptation process, and utilizes a rule-based system to identify different adaptation scenarios and k-means clustering to group students with similar adaptation requirements.

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Santoso Wibowo. "Evaluating Intelligent Building Systems Performance in Multicriteria Group Decision Making Problem." International Journal on Data Mining and Intelligent Information Technology Applications 3, no. 1 (March 31, 2013): 18–29. http://dx.doi.org/10.4156/ijmia.vol3.issue1.3.

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Schelling, Xavier, and Sam Robertson. "A development framework for decision support systems in high-performance sport." International Journal of Computer Science in Sport 19, no. 1 (July 1, 2020): 1–23. http://dx.doi.org/10.2478/ijcss-2020-0001.

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AbstractDecision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.

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Xu, Jiuping, and Zhibin Wu. "A discrete consensus support model for multiple attribute group decision making." Knowledge-Based Systems 24, no. 8 (December 2011): 1196–202. http://dx.doi.org/10.1016/j.knosys.2011.05.007.

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Introne, Joshua, and Luca Iandoli. "Improving decision-making performance through argumentation: An argument-based decision support system to compute with evidence." Decision Support Systems 64 (August 2014): 79–89. http://dx.doi.org/10.1016/j.dss.2014.04.005.

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Zhang, Li, Liang Gao, Xinyu Shao, Long Wen, and Jun Zhi. "A PSO-Fuzzy group decision-making support system in vehicle performance evaluation." Mathematical and Computer Modelling 52, no. 11-12 (December 2010): 1921–31. http://dx.doi.org/10.1016/j.mcm.2010.03.042.

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Journal articles on the topic 'Group decision making. Decision support systems. Performance'

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