In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship. First, the supremum and infimum incentives-based rule driven by trust relationship is defined. Based on the assumption that if incentive conditions are met, then experts will be willing to adjust their preferences, the incentive behavior-driven minimum adjustment consensus model is developed to generate optimal incentive-based recommendation preferences. Thus, the proposed incentive mechanism can effectively reduce the preference adjustment cost and promote group consensus reaching. Third, the updated trust relationships between experts are shown to be strengthen by the proposed incentive-driven preference revision. Consequently, the optimization model based on trust interaction relationship is constructed to obtain the final group preference matrix. Finally, a supplier selection case of high-end medical equipment is provided to illustrate the proposed method and show the rationality and advantages of the proposed methodology with both a sensitivity analysis and a comparison analysis.

For the first time, this study addresses the problem of evaluating propulsion technologies for sustainable road freight distribution from the multicriteria group decision-making perspective. It aims to help logistics companies involved in global supply chains significantly reduce GHG emissions from freight distribution activities. To evaluate sustainable propulsion technologies, this article introduces a novel group decision-making approach with DPL term sets, which takes into account the simultaneous occurrence of stochastic and nonstochastic uncertainty. Since the existing methods do not consider the dependency among multiple numbers of criteria, the evaluation results become insignificant. To overcome this, we employ the Archimedean operations on DPL elements to develop DPL weighted Archimedean Maclaurin symmetric mean operator. For measuring the weights of criteria, an optimization model using a cross-entropy measure of DPL elements is developed. A case study for one of the major logistics companies in Serbia is presented to exhibit the relevance of the suggested approach, as well as offer decision-making guidelines in real-world settings. The research findings show that hybrid electric trucks are the most advantageous propulsion technology solution to reduce GHG emissions from distribution activities. The superiority of the proposed approach is demonstrated through the comparative investigation.

Based on subjective possibilistic semantics, an agent's subjective probability mass function is dominated by a qualitative Possibility Mass Function (PossMF), which can also be transformed into a unique consonant mass function. However, the existing transformation method cannot maintain the consistency of combination rules, i.e., fusing PossMFs and consonant mass functions with same information content, respectively, the results no longer maintain the reversible transformation. To address the above issue, a novel belief functions transformation is proposed, which can be interpreted based on both Smets' canonical decomposition and Pichon's canonical decomposition. The proposed method is validated based on consistency of combination rules, the least commitment principle, and its application in the fusion of information. In addition, based on the two canonical decompositions, we extend the transformation to possibilistic belief structure, and offer a new perspective of relationship between possibilistic information and evidential information.

The relationship between emotion expressivity and psychological symptoms varies by race/ethnicity, and reduced expression of emotions has been implicated in risk for suicidal ideation. The present study examined differences in the relation between emotion expressivity and suicidal ideation through well-documented correlates of suicide risk (i.e., hopelessness, depressive symptoms) among Asian American subgroups compared with White emerging adults. A sample of 829 emerging adults, Ages 18 to 28 years, identifying as Asian American (27% East Asian, 18% South Asian, 11% Southeast Asian) or White (44%) completed measures of emotion expressivity, hopelessness, depressive symptoms, and suicidal ideation. Lower emotion expressivity was statistically associated with higher levels of suicidal ideation, via hopelessness and depressive symptoms, among White, East Asian, and South Asian American emerging adults, but not among Southeast Asian Americans, though this difference in mediation was not statistically significant. A focus on Asian Americans as a homogenous group occludes important ethnic differences in the relation between emotion expressivity and vulnerability to suicidal ideation. Ethnic differences in the function of emotion expressivity should be considered in suicide prevention and interventions among Asian American emerging adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

ABSTRACT The objective of this study is to develop knowledge on why some accredited programmes are more distinct. Accreditation and distinctiveness are both important in higher education: maintaining accreditation signals quality, while distinctiveness helps programmes meet their stakeholders’ preferences. Our application of two-stage valuation theory implies that programmes are higher on distinctiveness when they view distinctiveness as beneficial without it posing a substantial risk to their reaccreditation. We quantitatively test the implications of two-stage valuation theory through text analysis of 369 mission statements of business programmes with accreditation from the Association to Advance Collegiate Schools of Business (AACSB). We examined mission statements, because the AACSB requires that mission statements reflect programmes’ aspirations such as distinctiveness. The multisource data show an inverse U-shaped relationship between accreditation tenure and distinctiveness. The data also show that public programmes have lower distinctiveness than private programmes across lengths of accreditation tenure. This research contributes by helping to shift the discussion from whether reaccreditation helps or harms distinctiveness to how and when reaccreditation impacts distinctiveness. Further, it demonstrates that affiliation serves as a boundary condition on the effect of reaccreditation on distinctiveness.

Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions.

AbstractAbout 10% of the US population faces food insecurity, defined as “limited or uncertain access to adequate food.” On college and university campuses, food insecurity among students is often reported as three times higher than the population at large. Food insecurity on campus has been shown to reduce academic performance, and by extension, student retention. In response to this issue, many colleges and universities have implemented campus food pantries. This practice paper is a potential blueprint for higher education institutions to implement a campus food pantry, and to market the food pantry to students who experience food insecurity without stigmatizing them. The paper synthesizes lessons from implementation and insights from existing literature to promote the inclusion of food pantries as a tool for boosting student success and student retention.