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The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

This paper aims to address two fundamental questions: (1) How has Bahrain's industrial policy evolved during the 21st century? and (2) what factors contribute to this evolution?

Utilizing secondary data, this paper identifies key decision-makers responsible for economic policy in Bahrain and delineates the evolution of Bahrain's industrial policy throughout the 21st century. Subsequently, it employs a series of interviews with elite civil servants engaged in the formulation and implementation of Bahrain's economic policies to understand the reasons behind the observed changes.

Since assuming the role of Crown Prince in 1999, Sh. Salman bin Hamad Al Khalifa has been the key economic decision-maker in Bahrain. During the 21st century, Bahrain has shifted away from decisions closely aligned with the Washington Consensus towards those more in line with classical industrial policy. Interviews reveal that the private sector's underperformance in job creation, coupled with fiscal pressures, has driven this departure from the Washington Consensus. Moreover, the early successes of the interventionist Saudi Vision 2030 and Bahrain's own success in technocratically managing the COVID-19 pandemic have accelerated this transition.

Insights into the determinants of Bahrain's industrial policy can guide policymakers in refining future strategies. Recognizing the positive role of intellectual developments in academic economics literature becomes crucial for informed decision-making.

This paper fills a gap in the existing literature by providing answers to its research questions, particularly considering the significant changes witnessed in Bahrain's industrial policy post-pandemic.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

The paper attempts to construct a novel multi-objective grey hierarchical group consensus approach to deal with the group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among decision makers (DMs).

To deal with these group consensus problems consisting of hierarchical relationship and non-cooperative behaviors among DMs non-cooperative behavior in uncertain information systems, considering the influence of coordination cost and the degree of group consensus, based on the idea of grey situation decision-making, the authors establish a multi-objective grey hierarchical group consensus model, and design different invalid decision elimination rules for decision-making groups of different sizes, and use a case verifies the effectiveness and feasibility of the model.

With the continuous improvement of the coordination cost budget, the degree of consensus of all departments and the overall consensus tend to be stable, and will no longer change with the increase of the coordination cost budget. The cost required by each department is basically consistent with the response trend of the cost required to coordinate the overall situation to the pre-set lower limit of group consensus.

The proposed approach can succeed in identifying DMs' information, and mine the DMs' information and help make a relatively more scientific decision.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.

This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.

This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.

Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.

Autistic individuals are at increased risk of trauma exposure and post-traumatic stress disorder (PTSD). Diagnostic overshadowing, however, often results in PTSD symptoms being mislabelled as autistic traits. This study aims to develop professional consensus on the identification and assessment of co-occurring PTSD in autistic adults.

An online modified Delphi design was used to gather professionals’ perspectives on key aspects of the identification and assessment of PTSD in autistic adults. Data were gathered qualitatively in Round 1 and then synthesised using content analysis into a list of statements that were rated in Round 2. Statements reaching 60–79% consensus and additional suggestions were sent out for rating in Round 3. Consensus for the final statement list was set at 80% agreement.

Overall, 108 statements reached consensus. These form the basis of professional-informed recommendations to facilitate the identification and assessment of PTSD symptoms in autistic adults.

The final Delphi statements provide a framework to assist with the assessment and recognition of traumatic stress reactions in autistic adults presenting to mental health, diagnostic or social services.

To the best of the authors’ knowledge, this is the first study to explore the presentation and identification of PTSD in autistic adults (with and without intellectual disability), using a bottom-up approach informed by professional consensus.

Most prior studies treated human resource management (HRM) strength as a whole, while neglecting the dynamic interactions between distinct components (consensus, consistency and distinctiveness). The authors lack a deep understanding of how different components operate together to influence burnout. To address these gaps, this study aims to adopt signaling theory to investigate the interactions among different components and their impacts on employee burnout.

The authors collected time-lagged data from 231 full-time employees in manufacturing firms in Suzhou, China. The authors used the PROCESS Model 6 and hierarchical multiple regression to analyze the data.

This study found that HRM system consensus and consistency mitigate employee burnout, whereas HRM distinctiveness is not significantly related to burnout. Furthermore, the authors revealed that HRM system consistency (rather than distinctiveness) mediated the relationship between consensus and burnout. Moreover, the authors found the sequential mediating effects of HRM system distinctiveness and consistency on the association between consensus and burnout.

Considering that employees’ well-being problems may be debilitating and overwhelming during the COVID-19 pandemic, it is particularly ethical and timely for managers to direct attention to the role of HRM system strength in addressing employee burnout.

This study advances the HRM system literature by teasing out the interactions between the three pivotal components of HRM strength. Our study is among the first to empirically investigate the internal relationships between the meta-features of the HRM system and employee burnout. In doing so, the authors develop a more nuanced understanding of the collective nature of a strong HRM system that conveys a shared message about HRM to promote well-being.