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The purpose of this paper is to develop comprehensive criteria for evaluating fashion design schemes and used an integrated model which considers the interrelation between the clusters of evaluation and the influence between criteria for evaluating alternative fashion design schemes.

The integrated approach uses the advantages of all three methods: the Decision-Making Trial and Evaluation Laboratory (DEMATEL) can be used to analyse the interrelations between the major clusters of fashion design evaluation. The analytical network process can calculate the criterion weight that is adjusted based on the influence between different elements in the decision framework. The Visekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) determines the best fashion design by ranking a set of designs by using ten conflicting criteria.

Style is the decisive dimension because it is highly affected by other clusters. The comfort of the style is the most crucial criterion. “Veracious” is the best and most preferred design scheme.

The study develops the decisive cluster and criteria in designing a fashion design scheme. The proposed approach can be used as a decision analysis tool in fashion design and other fields and has various advantages (e.g. considering interrelations between clusters and influences between criteria, and ranking a set of alternatives), and therefore, is appropriate for practical circumstances.

This study proposes and demonstrates a novel approach to analyzing customer channel preferences and willingness-to-pay (WTP) in the dual sales channel (DSC) system involving direct online channels and conventional offline retailers, and to how the pricing decisions are made under specific game competition.

Questionnaire survey based on central composite experiment design was utilized to obtain primary data. The model for customer channel preferences and WTP was then built by using multinomial logistic regression. The propensity of a customer to make purchases in either channel estimated by using the logit model was inserted in the bilevel programming model to formulate and solve for the Stackelberg competition where the conventional retailer acted as a leader.

The study found that channel prices have nonlinear impacts on WTP and channel preference. The empirical results complement the mathematical formulation well where high-order own-price and cross-price effects on channel selection are generally not analytical tractable. Under the Stackelberg competition, the traditional retailer (as the leader) still achieves higher profits than the online facility.

The proposed framework provides an empirical approach that can easily address the competition model in the sales channel when complicated own-price or cross-price effects are present.

The present work provides a novel approach to analyze customer preference and WTP of the DSC systems. This alternative method simplifies the procedure for investigating and estimating price sensitivity, especially when the online and offline prices affect customer WTP and channel preferences nonlinearly. This model is also utilized in the game competition to facilitate data-driven price decision making to better formulate and understand real-world DSC problems.

The purpose of this paper is to compare various linear opinion pooling models for aggregating probability judgments and to determine whether Cooke's performance weighting model can sift out better calibrated experts and produce better aggregated distribution.

The leave‐one‐out cross‐validation technique is adopted to perform an out‐of‐sample comparison of Cooke's classical model, the equal weight linear pooling method, and the best expert approach.

Both aggregation models significantly outperform the best expert approach, indicating the need for inputs from multiple experts. The performance score for Cooke's classical model drops considerably in out‐of‐sample analysis, indicating that Cooke's performance weight approach might have been slightly overrated before, and the performance weight aggregation method no longer dominantly outperforms the equal weight linear opinion pool.

The results show that using seed questions to sift out better calibrated experts may still be a feasible approach. However, because the superiority of Cooke's model as discussed in previous studies can no longer be claimed, whether the cost of extra efforts used in generating and evaluating seed questions is justifiable remains a question.

Understanding the performance of various models for aggregating experts' probability judgments is critical for decision and risk analysis. Furthermore, the leave‐one‐out cross‐validation technique used in this study achieves more objective evaluations than previous studies.

Methods and techniques of aggregating preferences or priorities in the analytic hierarchy process (AHP) usually ignore variation or dispersion among experts and are vulnerable to extreme values (generated by particular viewpoints or experts trying to distort the final ranking). The purpose of this paper is to propose a modelling approach and a graphical representation to characterize inconsistency and disagreement in the group decision making in the AHP.

The authors apply a regression approach for estimating the decision weights of the AHP using linear mixed models (LMM). They also test the linear mixed model and the multi‐dimensional scaling graphical display using a case of strategic performance management in education.

In addition to determining the weight vectors, this model also allows the authors to decompose the variation or uncertainty in experts' judgment. Well‐known statistical theories can estimate and rigorously test disagreement among experts, the residual uncertainty due to rounding errors in AHP scale, and the inconsistency within individual experts' judgments. Other than characterizing different sources of uncertainty, this model allows the authors to rigorously test other factors that might significantly affect weight assessments.

This study provides a model to better characterize different sources of uncertainty. This approach can improve decision quality by allowing analysts to view the aggregated judgments in a proper context and pinpoint the uncertain component that significantly affects decisions.

The purpose of this paper is to investigate the range sensitivity of the analytic hierarchy process (AHP) and evaluate the effectiveness of using a bottom‐up approach to mitigate the possible range insensitivity bias in the AHP.

An experiment was conducted to test the normative range‐sensitivity of four different methods: the AHP with bottom‐up evaluation; direct ratio weights; swing weights; and trade‐off weights. Also, the significance of the range‐sensitivity effects and the differences among weighting approaches were rigorously tested using various statistical models.

Results show that the range sensitivities of AHP and direct ratio weights are significantly less than the range sensitivities of swing weights and tradeoff weights, suggesting that the bottom‐up evaluation approach might not be a feasible solution for the range‐insensitivity problem. This finding is consistent with the value‐comparison hypothesis proposed in an earlier study, and is partially supported by the theory of the multi‐dimensionality of attribute importance.

It is concluded that treating the attribute weights and performance scoring scales separately in the AHP or other multi‐attribute decision analysis models might lead to an arbitrary final ranking of alternatives. Therefore, it may be necessary to incorporate better elicitation procedures into the AHP models to ensure that attribute weights properly reflect the range or scale of measurement.

This study provides new evidence and issues words of warning of the range‐sensitivity effects in the multi‐attribute decision analysis.

The purpose of this paper is to investigate how expert overconfidence and dependence affect the calibration of aggregated probability judgments obtained by various linear opinion‐pooling models.

The authors used a large database containing real‐world expert judgments, and adopted the leave‐one‐out cross‐validation technique to test the calibration of aggregated judgments obtained by Cooke's classical model, the equal‐weight linear pooling method, and the best‐expert approach. Additionally, the significance of the effects using linear models was rigorously tested.

Significant differences were found between methods. Both linear‐pooling aggregation approaches significantly outperformed the best‐expert technique, indicating the need for inputs from multiple experts. The significant overconfidence effect suggests that linear pooling approaches do not effectively counteract the effect of expert overconfidence. Furthermore, the second‐order interaction between aggregation method and expert dependence shows that Cooke's classical model is more sensitive to expert dependence than equal weights, with high dependence generally leading to much poorer aggregated results; by contrast, the equal‐weight approach is more robust under different dependence levels.

The results suggest that methods involving broadening of subjective confidence intervals or distributions may occasionally be useful for mitigating the overconfidence problem. An equal‐weight approach might be more favorable when the level of dependence between experts is high. Although it was found that the number of experts and the number of seed questions also significantly affect the calibration of the aggregated distribution, further research to find the minimum number of questions or experts is required to ensure satisfactory aggregated performance would be desirable. Furthermore, other metrics or probability scoring rules should be used to check the robustness and generalizability of the authors' conclusion.

The paper provides empirical evidence of critical factors affecting the calibration of the aggregated intervals or distribution judgments obtained by linear opinion‐pooling methods.