Journal of Multi-Criteria Decision Analysis最新文献

This study presents an educational performance assessment of Brazilian state-level units using the technique for order preference by similarity to the ideal solution (TOPSIS). Although this technique is a well-established multi-criteria decision making (MCDM) model that can be applied across diverse areas, it has yet to be applied to an analysis of primary and secondary education. We assess how state-level units in Brazil perform in terms of primary and secondary educational inputs and outputs during the period from 2013 to 2017. We handle epistemic uncertainty with regard to weight definition using maximal information entropy. Additionally, a Tobit regression approach on performance scores is developed. Results show that region of the country plays an important role in determining educational performance. Moreover, results indicate that gross domestic product (GDP) is positively related to education scores while infant mortality is negatively associated with educational performance.

Due to the latest developments in the healthcare sector, the efficiency and effectiveness of healthcare services are being improved. This investigation aims to shortlist the organizational hindrances that influence the adoption of Healthcare 4.0 technologies and model the mutual relationship between them using the DEMATEL methodology. In addition, this study offers a cause-effect relationship between the barriers and helps to identify the most important challenges to the adoption of Healthcare 4.0 technologies. By reviewing the literature and consulting experts in the case domain, 10 hindrances were identified. The findings of the study highlighted three causal factors namely ‘Lack of vision and commitment from the top management’, ‘Hierarchical structure’, and ‘Lack of skilled workforce’ as critical ones that have the significant causality intensity; whereas, ‘Lack of R&D facilities’ was found be the least significant barrier which belongs to the effect category. It may be noted that the causal factors need to be addressed by the top management of the healthcare organizations for the successful adoption of Health 4.0. Further, the elimination of the causal factors identified by this study would help in reducing the cost of the treatment and enhance the patients' experience. In addition, this research aims to guide managers/decision-makers of healthcare organizations to deduce policies for the adoption of the latest technologies in the case sector.

Medical devices are introduced in the market with an ever-increasing rate, with important benefits for the whole society's health. In the greatest majority of cases, although representing the entry point for devices, healthcare local institutions/hospitals do not receive any macro-level guidance from (inter)national authorities for their assessment. Hence, in the absence of a methodological support, local institutions/hospitals started to assess devices autonomously, building on Health Technology Assessment (HTA) tools based on Multi-Criteria Decision Analysis (MCDA), in order to take into-account the multifaceted aspects connected with devices. However, the strategies described so far in the scientific literature for implementing MCDA in local-based HTA of medical devices suffer from a harsh methodological weaknesses – that is, the use of bespoke criteria for the specific device to be assessed - that severely affect the evaluation of medical devices. Within this scenario, this work proposes a new tool based on peer-to-peer IF-TOPSIS, intended for micro-level assessment of medical devices with the main objective of overcoming the above-mentioned critical issues and provide local institutions/hospitals with a general tool. An example of application of this tool in the choice between three neurological devices is shown. Contributions are both theoretical and practical. Theoretically, while proposing a general MCDA tool for micro-level HTA, we answer the call for the identification of key methodological principles for the local assessment of medical devices. At the practical level, this tool is readily implementable and can be adapted to consider the local idiosyncratic characteristics of the context where decisions have to be made.

The prosed study aims to provide COVID-19 critical success factors (CSF) associated with pandemic circumstances in the Indian healthcare industry (HCI). The CSF was identified via expert team inputs and a detailed literature review. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to determine the causal relationship between identified CSF. The methodology was supported by the case study of the Indian HCI. A total of 15 CSF in the Indian HCI during COVID-19 are identified and prioritized using the DEMATEL method. The findings indicate that the high-quality personal protective equipment (PPEs; LC8) and testing laboratories/facilities, centres, and kits (LC15) are the significant cause, and appropriate healthcare laws (LC13) are the least effect group. The study shows that policy and decision-makers need to emphasize on LC8 and LC15 CSF in the Indian HCI and act accordingly to win the battle against post-COVID-19 circumstance. The policy/decision-makers and healthcare administrations can identify the CSF and focus on that particular CSF. The identified CSF will help policy and decision-makers swiftly build up the HCI to cope with the future pandemic.

The Analytic Hierarchy Process (AHP) is a decision making method, which has as its greatest criticism the rank reversal effect. Here a new mathematical analysis of this method is performed, and three new results are highlighted. First, the method is formulated as a linear system of equations, where it is possible to assign a geometric interpretation, determine the number of possible solutions, and perform an sensitivity analysis based on the condition number of the matrix. Second, the causes of rank reversal can be encompassed by two mathematical aspects related to the properties of the matrix of AHP: high condition number and deficient rank. When the matrix is deficient rank, it is possible to obtain a condensed formulation of the AHP with a new full rank matrix. This guarantees greater stability to the method. Third, some mathematical results can be used as a robustness test for the matrix of AHP.

The Healthcare sector is increasing in importance and relative size in BRICS countries (Brazil, Russia, India, China, South Africa). Despite BRICS relevance, the financial performance of their healthcare companies has been scarcely studied. This research fills this literature gap not only by focusing on the impacts of such diverse business environments on the financial performance of healthcare providers but also by proposing a novel approach to estimate an overall financial performance index based on weighted additive utility functions given a set of financial performance criteria. Precisely, bootstrapped Singular Value Decomposition is the cornerstone for identifying an orthogonal base of rotated financial performance criteria, upon which partial utility functions (PUFs) are estimated using locally estimated scatterplot smoothing (LOESS) polynomial regression. A compromise weighting scheme between singular values and quadratic programming results for minimal covariance and joint entropy matrices of residuals was used for summing up the PUFs. Results indicate that the values of financial performance range between 0.7 and 0.85. We further find that current assets, level of debt and liability, the company's Tobin Q are related to the financial performance. Besides, business freedom, government integrity, tax burden, monetary freedom and government spending are also the determinants of financial performance.

The increasing attention to continuous auditing in practice has demonstrated its significance as an important field of scholarly inquiry. This research takes the two well-known continuous auditing systems, embedded audit module (EAM) and monitoring and control layer (MCL), to explore the key factors of successfully implementing a continuous auditing system. This study proposes a hybrid multiple criteria group decision-making (MCGDM) model, which employs decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) to determine the influential direction and criterion weight of continuous auditing system. We then use zero–one goal programming (ZOGP) to suggest the optimal allocation of the limited resources within the firm for the continuous auditing system. Finally, we present an empirical case to demonstrate the applicability of our proposed model. The proposed model guides business managers and executives to make proper judgment under dynamic market and technological environment.

Value in drugs do not always lies simply in them curing diseases: they may also be used to prolong life, improve its quality, and relieve patients' families. How much these elements matter should weigh on reimbursement decisions. Rare diseases often lack strong clinical evidence; hence, overlooking other elements that society values may lead to suboptimal decisions. We examine the preferences for treatments of non-oncological rare diseases among 16 experts in Poland with PAPRIKA—a choice-based elicitation method. Among seven attributes, clinical effect was found to be the most important, followed by returning the patient's family to normal functioning. Whether a treatment is used in children or any alternative treatment is available were the least important attributes. The respondents have a non-linear, diminishing preference for clinical effects, and an almost linear (negative) preference for cost. We develop a new method of sensitivity analysis that accounts for the non-uniqueness of part-worth utilities and the possibility of respondents answering inattentively to some questions, and we find our results to be robust. When looking at heterogeneity, three types of respondents deviating from mean-preferences emerge, in terms of what particular factors they place emphasis on: (a) cost and effectiveness, (b) to whom technology is offered (prognosis and availability of alternative), (c) the impact on family, safety, and caring for children. The part-worth utilities identified can be used to support multiple criteria decision-making in Poland.

Flood is one of the highly pronounced ruinous naturally occurring environmental hazards in which human lives and properties are gravely endangered. This study assessed areas prone to flood in Ibadan City, Nigeria. The study applied two multi-criteria analysis approach—Analytical Hierarchy Process (AHP) and the Fuzzy Analytical Hierarchy Process (FAHP) with the aid of GIS. Ten geomorphological physical characteristics agreed upon by experts as the major factors influencing the susceptibility of an area to floods formed the criteria maps used in this study. The natural breaks and geometrical interval clustering techniques were utilised to group the final flood-prone area maps into five categories—very low, low, moderate, high, and very high. The result indicated that over 50% of the study area either have low or moderate susceptibility to flooding. The clustering techniques showed some slight differences in the flood classifications where natural breaks consistently grouped more areas into the “very high” category and relatively fewer areas to “very low” category in comparison to the geometrical interval technique. Overall, at least 451 km² (about 15% of Ibadan) is highly or very highly prone to floods, and these areas are mainly concentrated in the urban and semi-urban regions of the study area. The findings and maps developed in this study would adequately serve as a planning guide for the policymakers on urban development and flood risk management to achieve sustainable development and management.