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Decision making is the task of selecting the most appropriate alternative among a finite set of possible alternatives with respect to some attributes. The attributes may be subjective or objective (or combination of both), depending upon the situation; requirements may also be conflicting. In practice, most of the real-world decision-making problems are based on subjective evaluation criteria which are basically ill-defined and vague. Since subjective human judgment bears ambiguity and vagueness in the decision making; application of grey numbers set theory may be proved fruitful in this context. The paper aims to discuss these issues.

Owing to the advantages of grey numbers set theory in tackling subjectivity in decision making; the crisp-TODIM needs to be extended by integrating with grey numbers set theory in order to facilitate decision making consisting of subjective data. Hence, the unified objective of this paper is to propose a grey-based TODIM approach in the context of decision making.

Application potential of grey-TODIM has been demonstrated through a case empirical robot selection problem. Result obtained thereof, has also been compared to that of existing grey-based decision support systems available in literature.

Application potential of grey-based decision support systems (grey-TOPSIS, grey analysis, grey-MOORA) have been highlighted in available literature resource. However, the shortcoming of these approaches is that they do not consider decision-makers’ risk attitude while decision making. TODIM method is derived from the philosophy of Cumulative Prospect Theory (CPT) which considers risk averting attitude of the decision maker in case of gain and risk seeking attitude in case of loss, while comparing dominance between two alternatives with respect to a particular criterion. Hence, this paper contributes a mathematical foundation of TODIM coupled with grey numbers set theory for logical decision making.

This paper proposes a new multi-criteria decision-making method, called the vital-immaterial-mediocre method (VIMM), to determine the weight of multiple conflicting and subjective criteria in a decision-making problem.

The novel method utilizes pairwise comparisons, vector-based procedures and a scoring approach to determine weights of criteria. The VIMM compares alternatives by the three crucial components, namely the vital, immaterial and mediocre criteria. The vital criterion has the largest effect on the final results, followed by the mediocre criterion and then the immaterial criterion, which is the least impactful on the prioritization of alternatives. VIMM is developed in two forms where the first scenario is designed to solve one-goal decision-making problems, while the second scenario embraces multiple goals.

To validate the method’s performance and applicability, VIMM is applied to a problem of sustainable supplier selection. Comparisons between VIMM, analytic hierarchy process (AHP) and best-worst method (BWM) reveal that VIMM significantly requires fewer comparisons. Moreover, VIMM works well with both fractional and integer numbers in its comparison procedures.

As an implication for research, we have added the development of the VIMM under fuzzy and grey environments as the direction for optimization of the method.

As managerial implications, VIMM not only provides less complex process for the evaluation of the criteria in the managerial decision-making process, but it also generates consistent results, which make VIMM a reliable tool to apply to a large number of potential decision-making problems.

As a novel subjective weighting method, there exist five major values that VIMM brings over AHP and BWM methods: VIMM requires fewer comparisons compared with AHP and BWM; it is not sensitive to the number of criteria; as a goal-oriented method, it exclusively takes the decision-making goals into account; it keeps the validity and reliability of the Decision-Makers’ (DMs’) opinions and works well with integer and fractional numbers.

Reaching decisions in a deliberative manner is of utmost importance for boards, as their decision-making impacts entire organisations. The current study aims to investigate (1) the quality of group decisions made by board members, (2) their confidence in, satisfaction with, and reflection on the decision-making, and (3) the effect of two discussion procedures on objective decision quality and subjective evaluations of the decision-making.

Board members of various Dutch non-profit organisations (N = 141) participated in a group decision-making task and a brief questionnaire. According to the hidden-profile paradigm, information was asymmetrically distributed among group members and should have been pooled to reach the objectively best decision. Half of the groups received one of two discussion procedures (i.e. advocacy decision or decisional balance sheet), while the other half received none.

Only a fifth of the groups successfully chose the best decision alternative. The initial majority preference strongly influenced the decision, which indicates that discussion was irrelevant to the outcome. Nevertheless, board members were satisfied with their decision-making. Using a discussion procedure enhanced participants' perception that they adequately weighed the pros and cons, but did not improve objective decision quality or other aspects of the subjective evaluation. These findings suggest that board members are unaware of their biased decision-making, which might hinder improvement.

Rather than using student samples, this study was the first to have board members participating in a hidden-profile task.

The purpose of this paper is to investigate the effect of the use of subjective (objective) performance measures on relevant organisational outcomes, namely perceived managerial discretion (PMD) and manager’s satisfaction with the performance measurement system (PMS). Furthermore, the paper analyses the indirect link between subjective vs objective measures and managers’ satisfaction through PMD.

To test the research hypotheses, a paper-based questionnaire was sent to Italian health care managers in Lombardy. Thus, a PLS-SEM analysis was performed on a data set of 97 Italian health care managers.

Empirical findings showed that objective measures are more capable of supporting the managerial perception of discretion when compared to more subjective ones such as “fads” and “fashions”, and that managers are more satisfied with the PMS when it is grounded on objective measures rather than subjective ones.

The paper operationalizes and empirically tests the measure of PMD, linking this to antecedents and consequences. It also extends the literature on subjectivity in the PMS, since it develops new knowledge on the choice between subjective and objective measures by applying this choice to a variety of PMS, whereas prior literature on objective vs subjective measures has mainly focussed on performance evaluation.

The regular occurrence of natural disasters elevates the need for an effective method to measure organizational preparedness in responding to the adverse impact of disasters. In this context, this paper presents a new decision support model to assess organizational disaster preparedness using both subjective and objective disaster preparedness criteria in a multi-criteria decision-making context.

The statistical variance method is integrated with the proximity value index (PVI) technique to determine priority scores in order to rank organizational disaster readiness.

The results of applying the integrated model developed herein enable decision-makers to make informed decisions for assigning priority ranking of organizational disaster preparedness in a simpler and more efficient way.

Human resource is the most impacting criterion affecting hospital preparedness in undertaking action to cure disaster victims.

This paper offers an exemplar of a simple and efficient decision-making process considering the subjectivity associated with decision-making as well as the objectivity of data used for determining the priority ranking of organizational disaster preparedness.

Integrating statistical variance method with the PVI technique is novel and it has not been presented in previous studies. In fact, this study is the first to integrate both methods for selecting the priority ranking of organizational disaster preparedness.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

Robot selection is a critical decision-making task frequently experienced in almost every industries. It has become increasingly complex due to availability of large variety of robotic system in the present market with varying configuration, specification and flexibility. Improper selection may yield loss for the company in terms of potential profit as well as productivity. Hence, selection of an appropriate robot to suit a particular industrial application is definitely a challenging task. The paper aims to discuss these issues.

During robot selection, different criteria-attributes need to be taken under consideration. Criteria may be subjective or objective or a combination of both, depending on the situation. Criteria many be conflicting, in the sense that some criteria may require to be of higher value (higher-is-better), i.e. beneficial; while, others should correspond to lower values (lower-is-better), i.e. adverse or non-beneficial. Hence, the situation can be articulated as a multi-criteria decision-making problem. The specialty of Tomada de Decisión Inerativa Multicritero (TODIM) method is that it explores a global measurement of value calculable by the application of the paradigm of non-linear cumulative prospect theory. The method is based on a description, proved by empirical evidence, of how decision makers’ effectively make decisions in the face of risk.

Hence, the present work has aimed to explore the TODIM approach for industrial robot selection. Assuming all criteria have been quantitative in nature; the paper utilizes two different numeric data sets from available literature resource in perspectives of robot selection. Procedural hierarchy and application potential of the TODIM approach has been illustrated in detail in this reporting.

Variety of tools and techniques have already been documented in literature to solve different kinds of industrial decision-making problems; however, it seems that application of TODIM has got limited usage. Hence, application potential of TODIM has been demonstrated here in light of a robot selection problem.

The purpose of this paper is to investigate and evaluate the subjective decision-making of pilots during final approach with varying degrees of experience for landing and go-around.

In this research, the “Lorenz Attractor” was modified and used to model the subjective decision-making of pilots during the final approach. For landing and go-around situations, “hesitation frequency” and “decision-making time” were calculated for the subjective decision-making of pilots.

In this research, the modified Chaotic Lorenz Model was used on MATLAB with varying degrees of experience, namely, student pilots, less-skilled pilots, experienced pilots and well-experienced pilots. Based on the outcomes, the less-skilled pilot needs nearly four times more decision-making time on landing or go-around compared to the well-experienced pilot during the final approach.

Operators (pilots, air traffic controllers) need to make critical and timely decisions in a highly complex work environment, which is influenced by several external elements such as experience level and human factors. According to NASA, 80% of aviation accidents occur due to human errors specifically over the course of the aviation decision-making process in dynamic circumstances. Due to the consequences of this research the operators' training should be redesigned by assisting flight instructors on the weaknesses of pilots.

This research explores the endogenous dynamics of the pilot decision-making process by applying a novel “Chaotic Lorenz Model” on MATLAB. In addition, the operator's total decision time formula was improved by including the decision reviewing time and external factors. Moreover, subjective decision-making model created by the current authors and Wicken's information model were modified to the highly automated systems.

Robot selection is basically a task of choosing appropriate robot among available alternatives with respect to some evaluation criteria. The task becomes much more complicated since apart from objective criteria a number of subjective criteria need to be evaluated simultaneously. Plenty of decision support systems have been well documented in existing literature which considers either objective or subjective data set; however, decision support module with simultaneous consideration of objective as well as subjective data has rarely been attempted before. The paper aims to discuss these issues.

Motivated by this, present work exhibits application potential of preference ranking organization method for enrichment evaluations (extended to operate under fuzzy environment) to solve decision-making problems which encounter both objective as well as subjective evaluation data.

An empirical case study has been demonstrated in the context of robot selection problem. Finally, a sensitivity analysis has been performed to make the robot selection process more robust. A trade-off between objective criteria measure and subjective criteria measure has been shown using sensitivity analysis.

Robot selection has long been viewed as an important decision-making scenario in the industrial context. Appropriate robot selection helps in enhancing value of the product and thereby, results in increased profitability for the manufacturing industries. The proposed decision support system considering simultaneous exploration of subjective as well as objective database is rarely attempted before.

This research reviews numerous studies of the relationship between consumer knowledge and external search in conventional marketing channels to investigate differences among these studies that have produced conflicting results. The findings provide a benchmark for future researchers and practitioners seeking to gain insight into consumer information search processes unfolding in the new environment of online, mobile, and social networking channels.

A meta-analysis of an extensive array of empirical studies of the relationship between consumer knowledge and external information search was conducted. Regression analysis was used to test whether certain characteristics in the studies can explain variability in the effect sizes in which effect sizes are entered as dependent variables and moderators as independent variables.

Objective and subjective knowledge tend to increase search, while direct experience tends to reduce search. Consumers with higher objective knowledge search more when pursuing credence products. However, they search relatively less when pursuing search products. Consumers with higher subjective knowledge are much more likely to search in the context of experience products, but as is the case for objective knowledge having little effect on search for experience products, subjective knowledge has no significant effect on information seeking for search products. In addition, objective knowledge facilitates more information search in a complex decision-making context while higher subjective knowledge fosters more external information search in a simple decision-marketing context. Finally, the findings indicate that the knowledge search relationship reflects strong linkage in the pre-Internet era.

Relatively little is known about how the relationship between knowledge and information search varies across different types of products in simple or complex decision-making contexts. This study begins to fill this gap by providing insight into the relative importance of objective knowledge, subjective knowledge, and direct experience in influencing consumer information search activities for search, experience, and credence products in simple or complex decision-making contexts.