Decision Maker's Attitude Research Articles

The knowledge and attitude of Ghanaian decision-makers and researchers towards health technology assessment.

Although health technology assessment (HTA) is intended to provide policymakers with objective information, the likelihood that a health decision-maker (HDM) will use this information is associated with their knowledge, role and perception of the HTA process. In Ghana, policymakers are working towards formalising the use of HTA, but HDM knowledge of and attitude towards HTA are not known. Between March and May 2016, we conducted in-depth interviews and used inductive thematic analysis to explore Ghanaian HDMs (n=23) and researchers' (n=4) perceptions of and barriers to HTA and identify ways to promote HTA. We compare our findings with those reported in previous studies conducted in low-and-middle-income countries. Common themes were that resources, political and cultural factors act as barriers to HTA use. Recommendations made in previous studies which were also identified in this study included the need for the development of both human and data capacity, allocating funds to HTA and stakeholder involvement in HTA processes. Specific recommendations made by Ghanaian HDMs and researchers in this study focused on the establishment of an HTA body: its location, the constitution of the appraisal team, the type of evidence to appraise and who makes the final decision. The findings provide important information in the context of current planning to institutionalise HTA in Ghana. Addressing the identified barriers will enable policymakers to maximise the chances of realising the expected benefits of HTA, as participants who are potential producers and end-users are likely to use what they have contributed to.

Qualitative analysis to determine decision-makers’ attitudes towards e-government services in a De-Facto state

PurposeThe manner in which people, businesses and governments perform is changing because of the spread of technology. Digitalization of governments can be considered a necessity as we are now entering the era of the Internet-of-Things. The advantages and disadvantages of electronic governments have been examined in several research studies. This study aims to examine the attitudes of decision-makers towards e-government. The research aims are as follows: to determine the problems related with e-government usage, to establish the factors which decrease the usage of e-government services and to propose recommendations for the effective application of e-government practices.Design/methodology/approachQualitative research has been used for the study. Participants were chosen by the snowball sampling method, and face-to-face in-depth interviews were conducted with all decision-makers. In-depth interviews are more efficient and enable the acquisition of better qualitative information, in-depth knowledge and statistics, as the distance between the interviewer and interviewee is reduced (Stokes and Bergin, 2006). Questions asked can be categorized under two sections, where the questions in the first section are related to the decision-maker’s management style/managerial proposition, and in the second section, technological questions are asked in terms of the preferred communication method and the decision-makers’ attitudes towards e-government practices.FindingsDecision-makers perceive electronic government to be important, while the level of importance is observed to be different among the decision-makers. Chronic problems exist in many countries, such as nepotism, where the decision-makers have conflicting arguments about e-government and the resulting effect on nepotism. Furthermore, the study also indicates that decision-makers are aware of the importance of mobile government, although they acknowledge that more time is required, as their country is still developing. Electronic voting is also perceived to be important, although the decision-makers believe that security and privacy issues need to be solved before related projects can be initiated.Originality/valueThis research can be a benchmark study for the decision-makers of small island developing states by means of e-government. The impediments preventing the effective application of e-government practices are also discussed in the study. This study will be useful to highlight the triggers and obstacles for e-government development in the context of a developing country. Internet penetration has increased significantly since the 2000s, and therefore, decision-makers need to consider the shift in citizens’ behaviour, such as the high usage of smartphones and the emergence of the Internet-of-Things (Kaya and Bicen, 2016; Kumar et al., 2017).

Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China

With growing investments in the state key laboratories (SKLs) of China, it is essential to assess the efficiency of the administrative departments regarding managing the SKLs of China. However, few studies have been conducted about the evaluation of the performance of Chinese administrative departments in managing SKLs. To fill this gap, this paper investigates the performance within 22 SKL administrative departments in China, based on a data envelopment analysis cross-efficiency prospect aggregation approach incorporating the risk preference of decision maker. The empirical results show the major findings: (a) serious imbalance exists regarding the investment of 22 SKL administrative departments; (b) there are great differences among the performance of 22 SKL administrative departments; (c) some SKL administrative departments face an insufficient situation regarding the outputs; (d) different risk attitudes of decision maker have the significant impact on the evaluation results of the 22 SKL administrative departments. Based on these findings, we provide several policy suggestions for the development of SKLs in China.

Multiple criteria decision making based on weighted Archimedean power partitioned Bonferroni aggregation operators of generalised orthopair membership grades

In this paper, a multiple criteria decision making (MCDM) method based on weighted Archimedean power partitioned Bonferroni aggregation operators of generalised orthopair membership grades (GOMGs) is proposed. Bonferroni mean operator, geometric Bonferroni mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are introduced into generalised orthopair fuzzy sets to develop the Bonferroni aggregation operators. Their formal definitions are provided, and generalised and specific expressions are constructed. On the basis of the specific operators, a method for solving the MCDM problems based on GOMGs is designed. The working process, characteristics, and feasibility of the method are, respectively, demonstrated via a numerical example, a qualitative comparison at the aspect of characteristics, and a quantitative comparison using the example as benchmark. The demonstration results show that the proposed method is feasible that has desirable generality and flexibility in the aggregation of criterion values and concurrently has the capabilities to deal with the heterogeneous interrelationships of criteria, reduce the negative influence of biased criterion values, and capture the risk attitudes of decision makers.

A three-way decision model based on cumulative prospect theory

In three-way decision, the description on the risk attitude of decision-makers is a focus topic. In this paper, we propose a novel three-way decision model based on cumulative prospect theory. First, with the aid of a reference point, the value functions are utilized to describe different risk appetites of decision-makers towards gains and losses. Second, the weight functions incorporate the nonlinear transformation of the conditional probability. The cumulative decision weights are decided by taking into account the increasing order of value functions. Then, with value functions and weight functions, the new decision rules of the proposed model are deduced based on the principle of maximizing the cumulative prospect value rather than minimizing the cost. Further, we analyze and prove the existence and uniqueness of thresholds of our model. Then, the decision rules are simplified based on the conditional probability and numerical solutions of thresholds, and the algorithm for deriving three-way decision rules is constructed. Finally, an illustrative example and a series of relevant comparisons are presented to illustrate and validate the effectiveness and feasibility of our model.

Multi-stage stochastic fuzzy random programming for food-water-energy nexus management under uncertainties

A hybrid inexact optimization model is developed for food-water-energy nexus system management with the consideration of complex uncertainties and decision makers’ risk tolerance. A multi-stage stochastic fuzzy random programming (MSFRP) model is tailored to tackle variables with deeper uncertainties, a mixture of fuzzy and random fuzzy characteristics. Allowing to reflect decision makers’ subjective opinion and risk preference, it can provide decision makers the tradeoff information between system benefit and risk attitude. The proposed model was applied to an agricultural area Shandong Province, China with the aim of maximum total system benefits. The valuable managerial insights on optimal cultivated land distribution, water resource allocation, and energy supply strategies are provided for decision makers under uncertainties. Meanwhile, the pesticide and fertilizer consumption for crop planting, and the carbon emission embodied in per unit crop supply are also quantitatively estimated. Moreover, by setting different water resource availability scenarios, the impacts of future water resource conditions on optimal management strategies under climate change are evaluated and discussed. The results suggested that rice would be the critical crop with the largest planting area for food security during the planning horizon. Under scarcer water resource conditions, the system benefits would reduce due to more desalination water consumption and planting strategy adjustment. However, it would lead to less carbon emission embodied in per unit crop supply and relieve local carbon emission control pressure. Compared to the conventional multi-stage stochastic programming, the developed MSFRP can be more effective to reflect the optimistic and pessimistic attitude of decision makers and deal with future scenario information with deeper uncertainties.

Risk-Based Multi-Attribute Decision-Making for Normal Cloud Model Considering Pre-Evaluation Information

An uncertain multi-attribute decision-making (MADM) problem is studied based on cloud models. Cloud models, referring to fuzziness and randomness, are utilized to depict evaluation and pre-evaluation information which can reflect the future development performance of alternatives. Because of bounded rationality, decision maker's (DM) risk attitudes should be considered when facing uncertainty. Thus, a behavioral MADM (BMADM) method is proposed by considering DM's risk attitudes and pre-evaluation. First, a distance measure for normal cloud models is developed with consideration of both DM's risk preferences and random distribution, aiming at making full use of information. Second, as a basis of applying prospect theory, positive ideal reference point is set by considering both evaluation and pre-evaluation information from three aspects: risk-averse, risk-neutral, and risk-seeking preference coefficients, in which the sign of distance is not necessary to determine. The third element is the establishment of an optimization model for handling incomplete attribute weights, following which is to obtain the ranking of alternatives. The final phase is the application of the proposed method to one case, along with sensitivity and comparison analyses, as a means of illustrating the applicability and feasibility of the new method.

Robust Spectral Risk Optimization When Information on Risk Spectrum Is Incomplete

A spectral risk measure (SRM) is a weighted average of value at risk where the weighting function (also known as risk spectrum or distortion function) characterizes a decision maker's risk attitude. In this paper, we consider the case where the decision maker's risk spectrum is ambiguous and introduce a robust SRM model based on the worst risk spectrum from a ball of risk spectra centered at a nominal risk spectrum. When the ball consists of step-like risk spectra, we show that the robust SRM can be computed by solving a linear programming problem. For the general case, we propose a step-like approximation scheme and derive an error bound for the approximation. As an application, we apply the proposed robust SRM to one-stage stochastic optimization with the objective of minimizing the robust SRM and propose an alternating iterative algorithm for solving the resulting minimax optimization problem. Moreover, to examine stability of the robust spectral risk optimization model with respect to perturbation of observed data from the underlying exogenous uncertainty in data-driven environments, we investigate statistical robustness of the model and derive sufficient conditions for the required stability.

A novel distance of intuitionistic trapezoidal fuzzy numbers and its-based prospect theory algorithm in multi-attribute decision making model.

The aim of this paper is to develop a new decision making method considering decision makers' psychological behavior for multi-attribute decision making problem under intuitionistic trapezoidal fuzzy environment. We first put forward a new distance measure of intuitionistic trapezoidal fuzzy numbers. Then combining with cumulative prospect theory, we develop a novel decision making method, which can consider risk attitude of decision makers. Finally, an example is given to demonstrate the effectiveness and practicability of the proposed method.

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

This paper develops a hybrid multiattribute decision‐making methodology for ranking project proposals (PPs) through a judicious usage of historical data of completed projects to determine attribute weights, enabling elimination of problems associated with projected data such as cost and schedule overruns of real‐world projects. The weights generated from data envelopment analysis are explicitly utilized for ranking PPs while allowing subjectivity to be ingeniously incorporated into the decision process. The new approach is implemented for ranking 25 PPs, and the rankings it yields are found indifferent to the decision maker's attitude, which ascertains the robustness of the ranking methodology.

Decision support system toward evaluation of resilient supplier

PurposeSupplier evaluation is a part of logistic management. In the present era, resilient supply chain performance (RSCP) assessment of the vendor enterprise is respected as a hot topic. The purpose of this paper is to enable the managers to map the performance in percentage system and also enabling managers for identifying the weak indices-metrics, which need to be improved up to ideal or standard level and strong indices-metrics.Design/methodology/approachThe authors found two research gaps via a literature survey. The first research gap revealed that the performance of a resilient supplier is computed solely in terms of a fuzzy mathematical scale. The articles are not yet published, which could measure the RSCP in percentage. The second research gap argued about the mitigation of the multi-level hierarchical resilient vendor/supplier evaluation framework for materializing RSCP and identifying weak and strong performing indices-metrics. To compensate the both research gaps, the authors developed a novel fuzzy gain-loss evolutionary computational approach to assess the performance of a firm in percentage. Next, a revised ranking technique coupled with trapezoidal fuzzy set based fuzzy performance importance index is implemented on the framework to seek weak and strong indices-metrics. The performance loss of each metric using the ideal solution concept considering the attitude of decision makers is also revealed.FindingsThe authors found the RSC performance of supplier firm 74 per cent, whereas performance loss 26 per cent, while actual performance is compared with standard fuzzy performance index (SFPI). Performance loss 26 per cent can be compensated by improving the performance of weak indices-metrics.Originality/valueThe novelty of the paper is that the authors used the ideal solution concept to compute the SFPI and compare it with actual FPI for evaluating the gain and loss of resilient supplier firm in percentage and identify weak and strong indices so that managers can improve the performance of weak indices. The work possesses the significant for all organizations, as research work enables the managers to map and improve the RSC performance of any vendor firm in future. The presented work considers the case of an automobile parts supplier industry to validate the developed approach.

Evaluation of City Sustainability from the Perspective of Behavioral Guidance

High-quality evaluation of city sustainability is an important part of city policy making and development. In this paper, we evaluated the sustainability of the 14 cities in Liaoning, China, from 2015 to 2017. Based on the comprehensive consideration of the interactions among the social, economic and environmental systems, the traditional evaluation indicator system is refined. We incorporate the attitude of decision makers into the evaluation model and propose an objective weighting method by considering data distribution to objectively guide the cities to develop towards the established goals. The empirical research results show that cities located in eastern Liaoning performed the best and in western Liaoning performed the worst. The performances of the 14 cities in Liaoning were not perfect. Both the evaluation values and growth rates of 7 cities (accounting for 50.00%) were lower than the overall average level. The evaluation values of the three systems of the 14 cities were not balanced. The evaluation values of the social, economic and environmental systems fluctuated within the range of [0.0159, 0.0346], [0.0151, 0.0677] and [0.0123, 0.0483], respectively. The social and economic systems of most cities supplied more for the environmental system than for the other system. Cities with higher environmental base rankings offered less supply to other systems. At the same time, we also provide some individualized and concrete suggestions for the guidance of city sustainable development. By comparing the empirical data with the reality, it confirms the credibility of the method and the recommendations in this paper.

How do product quality uncertainties affect the sharing economy platforms with risk considerations? A mean-variance analysis

The sharing economy is of increasing importance for various aspects of our society by providing people with cost-efficient and convenient access to services or products. However, the profitability and healthy development of the sharing economy platforms are greatly restricted by the intensive uncertainties arising from product quality. How to determine the optimal product quality improvement efforts and the optimal prices for the sharing platforms becomes crucial. Moreover, it is reasonable that many sharing economy platforms and consumers are holding a risk-averse attitude during decision making in the highly volatile market. In this paper, we employ the Mean-Variance theory to model the risk-averse attitudes of decision makers, and analytically derive the optimal average quality levels and prices that the platform should provide for the market. We also explore how the critical factors like product quality uncertainty and risk sensitivity affect the platform's equilibrium decisions and consumer surplus. Our analytical results reveal the importance of reducing the difficulty of improving product quality for enhancing the platforms' profitability and consumer surplus. Besides, we identify that it is not always wise for platforms to invest in improving product quality, especially when the related cost becomes increasingly expensive. Moreover, it is shown that the increasing risk aversion or product quality volatility will induce the platform to reduce his quality improvement efforts. Finally, we interestingly find that the platform will always suffer if he becomes risk averse from risk neutral, while consumers may benefit from this attitude change.

Association between interprofessional collaboration readiness and clinical decision making ability of midwifery students

Maternal mortality remains a major challenge to health system worldwide. This problem requires the attention of health practitioners and health professional educators including midwives. Effective clinical decision making is among the most important skills required by healthcare practitioners in the intra-profession and interprofessional context. This study aims to explore the midwifery student's clinical decision-making ability and attitude toward interprofessional learning. This study used quantitative design with cross-sectional approach. Subjects of research were 50 midwifery students. The instruments were The Readiness for Interprofessional Learning Scale (RIPLS) questionnaire; and an assessment rubric of the obstetric case study. Most of the students are not ready to collaborate with other health professions (68%), and have low and moderate clinical decision-making abilities (98%). There is a significant positive correlation (p 0.05) between student readiness for interprofessional collaboration with clinical decision-making ability. This study emphasizes that the ability of a midwife in clinical decision making, especially in emergency cases, is related to their readiness or experience in collaborating with other health professionals.

(1807-4565) Interval number ranking method considering multiple decision attitudes

Many interval number ranking methods cannot represent the different attitudes of decision makers with different riskappetites. Therefore, interval numbers are expressed in the Rectangular Coordinate System (RCS). After mining theinterval numbers in the RCS, the Symmetry Axis Compensation Factor, which is known as λ, was introduced, andthe Equivalent Function of the Goal Interval Number (GIN) was deduced. Thus, the interval number ranking methodconsidering symmetry axis compensation was defi ned along with its application procedures. Additionally, the feasibilityand effectiveness of this method were veri ed through examples. This method is intuitive and simple and can representmultiple attitudes of decision makers with different risk appetites.

Optimal location selection for offshore wind-PV-seawater pumped storage power plant using a hybrid MCDM approach: A two-stage framework

Constructing an economical wind-PV-seawater pumped storage (SPS) plant is crucial to promote the complementarity of wind and PV resources in time and space dimensions and to reduce energy abandonment caused by voltage instability. It is also beneficial to realizing long-time sustainable development objectives for offshore areas and even for the whole country. To select the optimal site of wind-PV-SPS power plants with massive difficulties lying in different attitudes of decision makers to loss risk, uncertainties of decision-making environment and various attributes of evaluation alternatives, a fuzzy multi criteria decision making based a two-stage evaluation mode is proposed. Firstly, two sets of criteria systems, representing veto selection of regional resources and the conditions of sustainable development described by triangular intuitionistic fuzzy numbers (TIFNs), are established to ensure the scientificality and comprehensiveness of the evaluation process. Then, alternatives are preliminarily selected through veto identification. Afterwards, criteria weights of the second evaluation stage determined by the entropy method are incorporated within TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) to rank potential wind-PV-SPS objects. Finally, to validate the effectiveness of the proposed model, a case study in China is conducted and the calculation result shows that Qingtian Bay in Zhou Shan is the best.

Convex approximations for two-stage mixed-integer mean-risk recourse models with conditional value-at-risk

In traditional two-stage mixed-integer recourse models, the expected value of the total costs is minimized. In order to address risk-averse attitudes of decision makers, we consider a weighted mean-risk objective instead. Conditional value-at-risk is used as our risk measure. Integrality conditions on decision variables make the model non-convex and hence, hard to solve. To tackle this problem, we derive convex approximation models and corresponding error bounds, that depend on the total variations of the density functions of the random right-hand side variables in the model. We show that the error bounds converge to zero if these total variations go to zero. In addition, for the special cases of totally unimodular and simple integer recourse models we derive sharper error bounds.

Density‐clusters ordered weighted averaging op erat or based on generalized trapezoidal fuzzy numbers

To the problem of multi-attribute decision making with fuzzy numbers, this paper proposes a new type of operator called the density-clusters ordered weighted averaging (OWA) operator based on generalized trapezoidal fuzzy (GTF) numbers. This operator is abbreviated as the GTF-DOWA operator. A primary characteristic of the GTF-DOWA operator is that it considers the implicit structure of the GTF numbers to be aggregated by grouping the numbers to various local clusters. We discuss the grouping methods of the GTF numbers using the centroids of the numbers. The cluster weights are determined by the combined consideration of the decision maker's attitude and the scale of each local cluster. In addition, we discuss the primary properties of the GTF-DOWA operator. Finally, a numerical example regarding the selection of optimal alternative is provided. The aggregations of the GTF-DOWA operator are compared with those of the weighted arithmetic averaging (WAA) operator and the OWA operator based on GTF numbers to illustrate the validity of the GTF-DOWA operator.

Analysis of emission taxes levying on regional electric power structure adjustment with an inexact optimization model - A case study of Zibo, China

In this study, an inexact two-stage chance-constrained programming (ITSCCP) model was provided for multiple electrical power system supply and demand management in Zibo City under uncertainties. Three scenarios about the electric power structure adjustment, renewable power generation, and the emission taxes were designed. Methods of two-stage stochastic programming (TSP) and inexact chance-constrained programming (ICCP) were incorporated into the developed model to tackle uncertainties in terms of various cost coefficients, decision maker's risk attitude which was described by interval values and probability distributions. Moreover, under the objective of cost minimize, the electrical power generation planning for each terms under different feasibility degrees (violating constraints or available resources situations) can be obtained. The results indicated that higher probability of violating system constraints would increase risk of system, but lower the total cost; the proportion of optimized thermal power generation and imported electricity would decrease, which could promote the energy conservation and emissions reduction in some degree. At the same time, the model results are valuable for decision-makers to tackle the uncertainty of the power generation schemes within a complicated energy system and make a desired compromise between the satisfaction degree of the economic benefits and feasibility degree of constraints.

Prospect Theory-Based Consistency Recovery Strategies with Multiplicative Probabilistic Linguistic Preference Relations in Managing Group Decision Making

Given the inherent characteristics of a practical case, a group decision making (GDM) support model integrated with prospect theory-based consistency recovery strategies based on multiplicative probabilistic linguistic preference information is proposed. Some basic concepts related to probabilistic linguistic term sets as well as multiplicative linguistic preference relations and prospect theory are reviewed for subsequent analysis. Multiplicative probabilistic linguistic preference relations (MPLPRs) and normalized MPLPRs are also defined to describe practical GDM problems. Subsequently, a consistency measure of MPLPRs is investigated to obtain a consistency level. Moreover, prospect theory-based consistency recovery strategies are explored and used to build a GDM support model that considers different risk attitudes of decision makers. Its applicability and effectiveness are validated by conducting an illustrative example and a comparative analysis with other existing methods in the context of energy site selection. Finally, conclusions are drawn.