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Government organizations often store large amounts of data and need to choose effective data governance service to achieve digital government. This paper aims to propose a novel multi-attribute group decision-making (MAGDM) method with multigranular uncertain linguistic variables for the selection of data governance service provider.

This paper presents a MAGDM method based on multigranular uncertain linguistic variables and minimum adjustment consensus. First, a novel transformation function is proposed to unify the multigranular uncertain linguistic variables. Then, the weights of the criteria are determined by building a linear programming model with positive and negative ideal solutions. To obtain the consensus opinion, a minimum adjustment consensus model with multigranular uncertain linguistic variables is established. Furthermore, the consensus opinion is aggregated to obtain the best data governance service provider. Finally, the proposed method is demonstrated by the application of the selection of data governance service provider.

The proposed consensus model with minimum adjustments could facilitate the consensus building and obtain a higher group consensus, while traditional consensus methods often need multiple rounds of modifications. Due to different backgrounds and professional fields, decision-makers (DMs) often provide multigranular uncertain linguistic variables. The proposed transformation function based on the positive ideal solution could help DMs understand each other and facilitate the interactions among DMs.

The minimum adjustment consensus-based MAGDM method with multigranular uncertain linguistic variables is proposed to achieve the group consensus. The application of the proposed method in the selection of data governance service provider is also investigated.

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.

Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.

A mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.

The case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.

First, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.

The purpose of this paper is to propose and tests a model that plant managers can use to develop cross-functional strategic consensus between purchasing, production, and logistics. The mechanisms studied are grounded in Organizational Information Processing Theory.

The model is tested using a cross-sectional survey of 120 manufacturing facilities. Path Analysis is used to determine the strength of the relationships and the model fit.

The mechanisms studied have a positive effect on the level of cross-functional strategic consensus. Some mechanisms have an effect on consensus on goals, while others have an effect on consensus on priorities. The results suggest that a plant manager must implement these mechanisms in combination to achieve the best result.

The survey respondents are all from US manufacturing facilities. Although reasonably representative of the US population of manufacturing firms, results of a similar study in other countries would further refine the knowledge concerning the effects of the mechanisms on developing strategic consensus at the operating level.

The results of this paper provide plant managers with guidance regarding the mechanisms that can enhance cross-functional strategic consensus. In a hypercompetitive and dynamic environment, these mechanisms can help the Plant Manager create a streamlined and efficient operation.

This paper presents practitioners with mechanisms that can promote consensus between key supply chain departments. The results highlight the need to implement combinations of mechanisms in order to address both dimensions of strategic consensus. For academics, it provides an empirical test of antecedents to strategic consensus at the operational level.

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.

The study aims to build up knowledge for collateral exploration of the classical Delphi survey method for assessing the industrial sustainability-related correction factor using a real field study in Nigeria.

The paper elicits the character and the operational approaches using an example study to provide a critical review of the method. It estimates the correction factor for appraisal purposes by transforming expert opinion into a valid group consensus.

The work considers the specific parameters of the method, design and analysis for interpretation to prove the reliability and the validity for the research results. Moreover, it emphasises that the validity of the traditional Delphi research demands cautious theoretical and practical applications by the coordinating researcher. The paper establishes the current validity and effectiveness of the classical Delphi method of foresight and streamlines their efficient implementation for theory building despite its numerous weaknesses.

It explores desirable futures for the method while analysing what is possible and probable.

The purpose of this paper is to use Delphi technique to assess the applicability of coupling various project delivery methods (PDMs) and contract strategies (CSs) for delivery of public building projects (PBP) in Ethiopia.

To achieve the objective of this research, an online Delphi study was designed, conducted and analyzed using various statistical procedures. Results were compared to CII’s project delivery and CS (PDCS) tool, a US-based PDM selection tool.

This study converged in the third round signifying a consensus on the importance of design–build and construction manager at risk as alternatives to design–bid–build. In addition, “controlling schedule growth” was deemed to be a major performance measure that needs to be considered for PBP in Ethiopia, a result consistent with the outcomes from CII’s PDCS tool.

Among existing studies, none to the best of the authors’ knowledge has focused on a holistic approach to identify the most appropriate pairings of PDMs and CSs for PBP which was critically needed due to the magnitude and high stakes of these projects. This research is a steppingstone toward gaining knowledge on how to approach these types of projects in fast-growing environments thirsty for new building projects such as in Ethiopia.

The purpose of this paper is to describe how the classic Delphi method can be adapted and structured to ensure that specific research questions are clearly addressed.

As part of a larger mixed method project, a modified Delphi study was undertaken to explore factors influencing publication and non-publication of mental health nursing research.

This paper reports brief findings from the Delphi study. However, its main focus is the methodological issues arising from the Delphi method.

The paper argues that the classic Delphi method can be adapted and structured to ensure that specific research questions are able to be clearly answered. The adaptations are pragmatic in approach and in keeping with the general principles underpinning the Delphi method, while successfully addressing the problems of attrition and previous criticism of homogenous panels.

This paper offers some practical solutions to issue arising from undertaking research using the Delphi method.