Venture capital group decision-making with interaction under probabilistic linguistic environment

A Multiattribute Group Decision-Making Method With Probabilistic Linguistic Information Based on an Adaptive Consensus Reaching Model and Evidential Reasoning

This article proposes a new multiattribute group decision-making (MAGDM) method with probabilistic linguistic information that considers the following three aspects: an allocation of ignorance information, a realization of group consensus, and an aggregation of assessments. To allocate ignorance information, an optimization model based on minimizing the distances among experts is developed. To measure the consensus degree, a consensus index that considers the information granules of linguistic terms (LTs) is defined. On this basis, a suitable optimization model is established to realize the group consensus adaptively by optimizing the allocation of information granules of LTs with the particle swarm optimization (PSO) algorithm. With an objective to reduce the information loss during aggregation phases, the process of generating comprehensive assessments of alternatives with the evidential reasoning (ER) algorithm is presented. Therefore, a new method is developed based on the adaptive consensus reaching (ACR) model and the ER algorithm. Finally, the applicability of the proposed method is demonstrated by solving a selection problem of a financial technology company. Comparative analyses are conducted to show the advantages of the proposed method.

A flexible group decision-making method for green supplier selection integrating MABAC and CRITIC method under the linguistic Z-numbers environment

This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective.

Interval-valued hesitant fuzzy TODIM method for dynamic emergency responses

Emergency responses bear the characteristics of uncertainty and possess multi-attributes in decision making. This paper applies the interval evidential reasoning approach and the interval-valued hesitant fuzzy TODIM (IVHF-TODIM) method to tackle the dynamic emergency decision-making problem. We introduce a function to obtain the gain and loss degrees through the geometric area method. The gain and loss matrices of the interval belief degrees are found probabilistically. A new approach to obtaining the dominance degree matrix is proposed. From the IVHF-TODIM method, the overall dominance degree is established to provide the ranking of the decision alternatives. A recent case of selecting an emergency decision alternative for a large bushfire is used to validate the proposed method, followed by a comparative analysis.

From venture capital towards venture firm innovation performance: evidence from SMEs of China

Based on information asymmetry, agency theory and resource-based view (RBV), this study investigates the impact of venture capital (VC) on venture firm innovation performance, ascertains the extent to which VC affects venture firm innovation performance and finds the mediating effect of management incentives. Constructing a sample of a novel panel dataset of firms listed on the SME Board of China, we examined a sample of 927 start-ups between 2008 and 2017, showing a notable negative relationship between VC and Patent, and a positive relationship between VC and total factor productivity (TFP), providing stable evidence that VC could not spur firm patent directly, but facilitate the commercialization of innovation. Moreover, it shows that management equity incentives (MEI) and management cash incentives (MCI) playing significant positive mediating role between VC and TFP, while there is no mediating effect between VC and Patent. Findings of this study strengthen the experience of VC and suggest how practitioners of SMEs to enhance the commercialization of innovation, considerably extends our understanding of the impact of VC on venture firm innovation performance.

QUALIFLEX method for MAGDM with probabilistic uncertain linguistic information and its application to green supplier selection

Probabilistic uncertain linguistic sets (PULTSs) have extensively been employed in multiple attribute group decision making (MAGDM)problem. The QUALIFLEX method, which is relatively a novel MAGDM technique, aims to obtain the optimal alternative. This paper proposes the probabilistic uncertain linguistic QUALIFLEX (PUL-QUALIFLEX) method with CRITIC method. To show the effectiveness of the designed method, an application is given for green supplier selection and the derived results are compared with some existing methods. Thus, the advantage of this proposed method is that it is simple to understand and easy to compute. The proposed method can also contribute to the selection of suitable alternative successfully in other selection issues.

Green supplier selection with an uncertain probabilistic linguistic MABAC method

In recent years, with the increased voice for protecting the environment by the people all over the world, the governments also have actively adopted more and more measures to further promote environmental conservation and sustainable development. Traditional procurement approaches have not well updated to the current needs of the society, especially for the retail industry which is in relation to the national economy due to numerous products and different suppliers being involved. Therefore, the need for green procurement is more important. The qualified green supplier selection is the core of green procurement, which is the utmost importance in the business competition throughout the supply chain in today’s strong business competition. Thus, in order to obtain the optimal green supplier, integration of Entropy weights and multi-attributive border approximation area comparison (MABAC) under uncertain probabilistic linguistic sets (UPLTSs) has offered a novel integrated model, in which information Entropy is utilized for calculating objective weights with UPLTSs to acquire the final ranking result of green supplier. Besides, so as to indicate the applicability of devised method, it is confirmed by a numerical case for green supplier selection. Some comparative studies are made with some existing methods. The proposed method can also serve for selecting suitable alternative successfully in other selection problems.

MABAC method for multiple attribute group decision making with probabilistic uncertain linguistic information

In recent years, ecological problems have become increasingly serious which are forcing people to give up the past high investment, high consumption and high emission development to promote green growth, implement the green new deal and pay attention to green supply chain research and practice. Therefore, in order to attach great importance to the economic and environmental benefits, enterprises should implement green supply chain and “green” change which has become the trend and urgent. Thus, in order to obtain an optimal green supplier, integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic uncertain linguistic sets (PULTSs) has offered a novel integrated model, in which information entropy is utilized for calculating objective weights with PULTSs to acquire the final ranking result of green supplier. Besides, so as to indicate the applicability of devised method, it is confirmed by a numerical case for green supplier selection. Some comparative studies are made with some existing methods. The proposed method can also serve for selecting suitable alternative successfully in other selection problems.

Probabilistic Hybrid Linguistic Approaches for Multiple Attribute Group Decision Making with Decision Hesitancy and the Prioritization of Attribute Relationships

Focusing on ill-structured multiple attribute decision-making (MADM) problems, including decision hesitancy and attribute prioritization relationships, this paper investigates appropriate approaches for decision making. Firstly, we introduce the probabilistic hybrid linguistic term set (P-HLTS) for capturing probabilistic preferences about possible linguistic labels belonging to a wide range of hesitant linguistic term sets. Entropy and distance measurements for P-HLTS are developed without arbitrary complementing operations. To facilitate decision making with attribute prioritization relationships, we present a probabilistic uncertain balanced linguistic-prioritized weighted average (PUBL-PWA) operator and the probabilistic uncertain balanced linguistic-induced prioritized ordered weighted average (PUBL-IPOWA) operator. In terms of the strength of the above tools, we further construct two multiple attribute group decision-making (MAGDM) approaches under P-HLTS environments, namely, an approach for decision-making situations where attribute prioritization relationships are known in advance and the relative importance of decision makers (DMs) or decision-making units (DMUs) is not required for consideration, and second approach for decision-making situations where both attribute prioritization relationships and the weighted vectors of DMs or DMUs are explicitly unknown. In general, our proposed approaches are more flexible and practical when considering heterogeneous opinions, avoiding information distortion brought about by complementing operation-based distance measures. Furthermore, illustrative application studies are conducted to verify our developed approaches.

Supplier Selection of Medical Consumption Products with a Probabilistic Linguistic MABAC Method

In order to obtain an optimal medical consumption product supplier, the integration of combined weights and multi-attributive border approximation area comparison (MABAC) under probabilistic linguistic sets (PLTSs) has offered a novel integrated model in which the CRiteria Importance Through Intercriteria Correlation (CRITIC) method is employed for calculating the objective weights of various attributes and the MABAC method with PLTSs is used to acquire the final ranking result of a medical consumption product supplier. Additionally, so as to indicate the applicability of the devised method, this model is confirmed by a numerical case for the supplier selection of medical consumption products. Some comparative studies are made with some existing methods. The proposed method can also successfully select suitable alternatives in other selection problems.

Shadowed Sets-Based Linguistic Term Modeling and Its Application in Multi-Attribute Decision-Making

For many multi-attribute decision-making (MADM) problems, linguistic variables are more convenient for people to express the attribute values. In this paper, a novel shadowed set-based method is proposed to deal with linguistic terms, where the linguistic term sets are symmetrical both in meaning and form. Firstly, to effectively express the linguistic variables, we develop a data-driven method to construct the shadowed set model for the linguistic terms. Secondly, the Pythagorean shadowed set is defined, and some theorems are subsequently explored. Thirdly, we propose the score function of the Pythagorean shadowed number and develop a new MADM method on the basis of the Pythagorean shadowed set. Finally, a case study of the supplier selection problem is provided to illustrate the effectiveness of the proposed method, and the superiority of our method is demonstrated by comparison analysis.