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1 – 10 of over 161000Rajat Kumar Behera, Pradip Kumar Bala, Prabin Kumar Panigrahi and Shilpee A. Dasgupta
Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy…
Abstract
Purpose
Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.
Design/methodology/approach
A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.
Findings
The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.
Research limitations/implications
Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.
Originality/value
This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.
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Giles D’Souza and Arthur Allaway
The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and…
Abstract
The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and product line management. Data‐driven modeling describes a process of model‐building wherein models are created that fit the dynamics of the data rather than assuming a priori relationships among brands and their marketing mix elements. Based on a combination of time‐series and econometric modeling methods, these models can significantly improve a modeler’s ability to capture marketplace structure and dynamics. Although more complex than their predecessors, the capabilities of these new data‐driven decision support models make them potentially very powerful tools, improving intuition and managerial understanding while suggesting improved decision alternatives. Develops such a model using detailed multiproduct retail data and demonstrates its capabilities.
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Model management systems (MMS) empower decision makers throughout the problem‐solving phases by providing operations research and management science (OR/MS) models as well as the…
Abstract
Model management systems (MMS) empower decision makers throughout the problem‐solving phases by providing operations research and management science (OR/MS) models as well as the knowledge to build or use such models. Managerial problem solving typically involves a wide range of modeling activities, i.e., definition, retrieval, modification, execution, modification, and integration of decision models. This research stems from the basic premise that, given the problem, decision aiding software such as MMS can reach its highest level of performance when the necessary modeling activities are adequately supported, subsequently enhancing the quality of the decisions made by the users. Reported in this paper are the results from an experiment involving two versions of MMS used by naïve modelers in two decision‐making settings. Through this study, we learn that the decision‐making behavior of software users, especially the way they develop their decision strategies, is considerably influenced by the capability of the software.
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Introduction The history of business computer systems development indicates a trend towards the increasing awareness of the potential for man‐machine synergy in some form. Until…
Abstract
Introduction The history of business computer systems development indicates a trend towards the increasing awareness of the potential for man‐machine synergy in some form. Until the early 1970s, the principal impact of computing had been upon the lower organisational levels and on the more structured tasks of the organisation, but from that time on there has been an increasing movement towards amalgamating computer power and management science in an attempt to aid the decision maker facing complex and unstructured decision tasks. More recently, such a blend has come to be termed a decision support system.
Leonardo Ensslin, Clarissa Carneiro Mussi, Sandra Rolim Ensslin, Ademar Dutra and Lydia Pereira Bez Fontana
The purpose of this paper is to support the management of organizational knowledge retention through a multi-criteria decision aiding–constructivist model.
Abstract
Purpose
The purpose of this paper is to support the management of organizational knowledge retention through a multi-criteria decision aiding–constructivist model.
Design/methodology/approach
This exploratory and descriptive case study presents a decision support model guided by the constructivist approach and proactive in its operationalization.
Findings
The objectives and concerns of decision-makers regarding the retention of organizational knowledge are identified and organized into six strategic areas of concern, namely, recognition, knowledge dissemination, organizational culture, succession of professionals, management of vulnerability origins and knowledge management; a multi-criteria model is constructed and operationalized by a cluster of cardinal scales, showing and measuring the status quo of the performance profile, both in a local and global way, to support the management of the organization's knowledge retention; activities are classified into three performance levels (compromising, competitive and excellent), supported by graphical and numerical evidence; and the process to generate actions to improve the performance of critical activities and create the conditions to maximize the results of the organization is illustrated.
Practical implications
Based on the model, decision-makers are now aware of the essential aspects to support knowledge retention management, enabling them to monitor the current situation and proactively respond to ensure that the current knowledge potential is maintained and exploited.
Originality/value
Use of a constructivist approach to support the management of knowledge retention, incorporating into the model the specifics of the context and the values of its managers, and thus giving it legitimacy.
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Saeed Rouhani, Amir Ashrafi, Ahad Zare Ravasan and Samira Afshari
Decision support (DS), as a traditional management concept, have had a remarkable role in competitiveness or survival of organizations and nowadays, business intelligence (BI), as…
Abstract
Purpose
Decision support (DS), as a traditional management concept, have had a remarkable role in competitiveness or survival of organizations and nowadays, business intelligence (BI), as a brand modern impression, has various contributions in supporting decision-making process. Although, a variety of benefits are expected to arise from BI functions, researches, and models that determining the effect of BI functions on the decisional and organizational benefits are rare. The purpose of this paper is to study the relationship between BI functions, DS benefits, and organizational benefits in context of decision environment.
Design/methodology/approach
This research conducts a quantitative survey-based study to represent the relationship between BI capabilities, decision support benefits, and organizational benefits in context of decision environment. On this basis, the partial least squares (PLS) technique employs a sample of 228 firms from different industries located in Middle-East countries.
Findings
The findings confirm the existence of meaningful relationship between BI functions, DS benefits, and organizational benefits by supporting 15 out of 16 main hypotheses. Essentially, this research provides an insightful understanding about which capabilities of BI have strongest impact on the outcome benefits.
Originality/value
The results can provide effective and useful insights for investors and business owners to utilize more appropriate BI tools and functions to reach more idealistic organizational advantages. Also it enables managers to better understand the application of BI functions in the process of achieving the specified managerial support benefits.
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Computer‐aided decision‐support tools are part and parcel of the emergency planning and management process today. Much is dependent on using modern technology to gather and…
Abstract
Computer‐aided decision‐support tools are part and parcel of the emergency planning and management process today. Much is dependent on using modern technology to gather and analyse data on damage assessment, meteorology, demography, etc. and provide decision support for prevention/mitigation, response and recovery. Diverse technologies are merged to provide useful functions to aid the emergency planner/manager. Complexities arise when attempting to link several streams of technology to achieve a realistic, usable and reliable decision‐support tool. This discussion identifies and analyses the challenging issues faced in linking two technologies: simulation modelling and GIS, to design spatial decision‐support systems for evacuation planing. Experiences in designing CEMPS, a prototype designed for area evacuation planning, are drawn on to discuss relevant managerial, behavioural, processual and technical issues. Focus is placed on modelling evacuee behaviour, generating realistic scenarios, validation, logistics, etc. while also investigating future trends and developments.
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Focuses on consulting experiences utilizing simulation approachesthat capture decision‐making processes in formulations that aretransparent to the general manager. Examines the…
Abstract
Focuses on consulting experiences utilizing simulation approaches that capture decision‐making processes in formulations that are transparent to the general manager. Examines the dual benefit of modelling in terms of not only providing forecasts and an objective framework for quantitative evaluations, but also in the softer sense of building consensus in management teams. Casts these experiences against theories of effective group decision making, and other decision support examples which focus on the use of models. Reconciles the circumstances of the case with the conditions specified for effective group working and suggests that the greatest contribution may be made to consensus decision making when the whole modelling approach, not just access to model outputs, is integrated into the decision‐making process, and where the model complexity is commensurate with the task complexity and the task familiarity of the management group.
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M. Marzouk and O. Moselhi
A reliable estimate of markup is essential for successful bid proposals. This paper presents a decision support model for construction bidding. The developed model can assist…
Abstract
A reliable estimate of markup is essential for successful bid proposals. This paper presents a decision support model for construction bidding. The developed model can assist contractors in estimating markup, and owners and/or their agents in evaluating bid proposals. The model is generic and can be used as a tool to evaluate different alternatives in engineering, procurement, and construction. It utilizes the multi‐attribute utility theory and the analytic hierarchy process and makes use of their advantages. Unlike models developed for similar purposes, the proposed model provides a decision support environment for the two functions; that is, estimating markup and evaluating bids. It also enables the user to construct the decision hierarchy that best suits his/her company’s business environment and bidding strategy in a flexible manner. It accounts for the decision maker’s attitude towards risk. Two numerical examples are presented to demonstrate the use and capabilities of the proposed model.
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The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in…
Abstract
Purpose
The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices.
Design/methodology/approach
The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS.
Findings
Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products.
Research limitations/implications
DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations.
Originality/value
This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.
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