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The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set.

To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model.

The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules.

The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers.

The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

With respect to multi-stage group risk decision-making problems in which all the attribute values take the form of grey number, and the weights of stages and decision makers are unknown, the purpose of this paper is to propose a new decision-making method based on grey target and prospect theory.

First, the sequencing and distance between two grey numbers are introduced. Then, a linear operator with the features of the “rewarding good and punishing bad” is presented based on the grey target given by decision maker, and the prospect value function of each attribute based on the zero reference point is defined. Next, weight models of stages and decision makers are suggested, which are based on restriction of stage fluctuation, the maximum differences of alternatives and the maximum entropy theory. Furthermore, the information of alternatives is aggregated by WA operator, the alternatives are selected by their prospect values.

The comprehensive cumulative prospect values are finally aggregated by WA operator, alternatives are selected or not are judged by the sign of the comprehensive prospect theory, if the prospect value of alternative is negative, the corresponding alternative misses the group decision makers’ grey target, on the contrary, if the prospect value of alternative is positive, the corresponding alternative is dropped into the group decision makers’ grey target, the alternative with positive prospect value whose value is the maximum is selected.

Compared with the traditional decision-making methods using expected utility theory which suppose the decision makers are all completely rational, the proposed method is based on irrational which is more in line with the decision maker’s psychology. And this method considers the decision maker’s psychological expectation values about every attribute, different satisfactory grey target about attributes will directly affect decision-making result.

This paper aims at multi-attribute and multi-program group decision making when the attribute weights are completely unknown and the attribute value information is in the form of the interval number.

This is an artificial intelligence algorithm for designing information gathering in group decision making. The authors propose the nonlinear programming model to gather information based on plant growth simulation algorithm (PGSA). The authors collect each program on each attribute group decision preference ordering interval and then use them to find the preference vector and the preference matrix. The entropy method is used to determine the weight of each attribute by the constructed preference matrix. According to the possibility degree matrix of each attribute, the combined effect vector is established by the priority weight vector method, which sorts and selects the best decision making program.

To the authors' knowledge, the application of PGSA in the field of management decisions to collect program on each attribute group decision making preference interval number is the first trial in literature. It has retained more valuable decision making information from all experts without distortion.

In practice, a real number may not be an accurate representation, but only gives a range of values to describe the attributes. This study provides a useful measurement of interval number information for managers to evaluate military science, venture capital, and environmental assessment, etc.

The methodology considers the complete information to ensure no information distortion even with large and complex systems. The authors adopt computer artificial intelligence algorithms to obtain the objective evaluation, which is meaningful for both research studies and practical use.

The purpose of this paper is to propose a dynamic multi-attribute decision-making method based on the prospect theory for dealing with the dynamic multi-attribute decision-making problem with three-parameter interval grey number.

First, the kernel and comparison rule of three-parameter interval grey numbers are defined, which are the basis of collecting and sorting grey numbers. Next, the prospect value function is determined in view of the decision-making information with different time points as the reference points. Then, an optimal model for solving the attribute weight and time weight is constructed based on the grey entropy principle.

The paper provides a dynamic grey interrelation decision method based on the prospect theory with three-parameter interval grey number, and the example analysis shows that the method proposed in this paper has validity and rationality.

If we have a better understanding of the weights of different reference points, it is possible to receive a more reasonable expression for the comprehensive prospect utility value function.

The paper provides a grey interrelation decision method based on the prospect theory, which can help the decision maker deal with the dynamic multi-attribute decision-making problems under the uncertain environment.

The paper proposes the kernel and ranking method of three-parameter interval grey number, and uses different time points as the reference points to define the prospect value function. Furthermore, this paper structures a dynamic grey interrelation decision method with three-parameter interval grey number based on the prospect theory.

Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

The paper aims to research the multi-attribute decision-making in which the attribute values are interval grey numbers and the maximum probability of the value of grey number is known.

First, the authors define deviation degree and dominance relation of three-parameter interval grey number and get the equivalence between the dominance relation of decision-making object and the sum of three values of three-parameter interval grey number. Then, considering the uncertainty of goal weight, the authors construct multi-index optimization model based on deviation degree and get the goal weight.

The authors prove the rationality and effectiveness of decision-making methods by examples and give a new thought for grey multi-attribute decision-making methods.

As a paper research on theory, it offered a new multi-attribute decision-making method with three-parameter interval grey number.

This chapter seeks to better understand the skills and competencies that public relations (PR) professionals use in contributing to organisational decision-making processes. The data were collected by interviewing Finnish professionals using thematic semi-structured interviews. Overall, the results highlight a deep understanding of organisation management and decision-making processes. The most important competencies were business understanding and target group oriented thinking. The findings indicate that important skills are related to writing and social media. Regarding personal attributes, interaction and tolerance to criticism were acknowledged as most crucial. The conclusions suggest that if professionals analyse and review their skills, competencies and personal attributes related to decision making, this will support organisational performance and strengthen the added value of PR function. A reflection on the strengths and weaknesses of their own traits helps professionals enact their expected role in organisational problem solving and decision making.

For the multi-stage and multi-attribute risk group decision-making problem, the attribute weight, decision-maker weight and time weight are unknown. The attribute value is grey information. The purpose of this paper is to discuss a decision-making method.

Analysis techniques and the theory about distance degree are used to determine the decision-maker weight within single stage. Grey relational analysis method is applied to determine the attribute weight. Moreover, the uncertainty of time weight and the proximity between the attribute value and positive/negative value are taken into account. A multi-objective optimization model is established based on maximum entropy to obtain time weights, so the comprehensive value is determined.

An example shows the effectiveness and practicability.

For a decision-making process, the results are different in different periods. This method is computationally very simple, easily comprehensible.

The purpose of this paper is to research attribute reduction and decision making by gray dual‐information, taking into account the attribute reduction of attribute decision unknown for the interval gray numbers.

The authors obtain the attribute weights considering the consistency of experts’ judgment matrixes and the decision matrixes with gray information. They propose some experts’ attribute reduction ideas based on interval gray numbers of rough set. With the help of experts’ decision information, they consider attribute uncertainty ratio and attribute value ratio to reduce attribute. Finally, a numerical example shows its feasibility.

Some experts’ attribute reduction ideas are proposed based on interval gray numbers of rough set. With the help of experts’ decision information, attribute uncertainty ratio and attribute value ratio to reduce attribute can be considered.

Attribute reduction is keeping classified information systems under the same conditions and deleting redundant and irrelevant or unimportant attributes in order to solve the problem of decision making. This paper considers the attribute reduction based on gray dual‐information.

The purpose of this study is to examine the impact of key decision-making attributes on consumers’ choice of accommodation among and between hotels and Airbnb.

The study used a choice-based conjoint approach using 21 key decision-making factors that impact consumers’ choice of accommodation across five segments ranging from economy to luxury. Latent class estimation was used to identify segments of respondents who tend to have similar preferences for accommodation.

The results showed the presence of a consistent pattern of decision-making across the five accommodation segments, culminating in a hierarchy of importance in accommodation choice. The 21 key decision-making attributes comprised three tiers in order of decreasing importance: quality and service, amenities, and accessibility and safety. Further, latent class analysis indicated the presence of a hotel group and an Airbnb group of customers, which allowed us to identify how both types of providers might maximize the value of their offers to encourage customer switch.

The accommodation landscape is extremely dynamic (particularly as the COVID-19 pandemic unfolds) and complex. The present study cannot capture all of its intricacies but provides an invaluable foundation for future research on the topic of consumer choice in an evolving and competitive accommodation market.

Extant research on accommodation choice has focused on hotels or Airbnb only. Moreover, research that has considered both types of accommodation simultaneously is limited in its conceptual and methodological scope. The present study synthesizes the fragmented literature on consumers’ accommodation choices and offers a holistic and coherent schematic – the hierarchy of importance in accommodation choice – that can be used by future researchers and practitioners alike.