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Tables are widely used to store information. It belongs to table decision making to choose the pivotal fields or partition fields into conditions and decisions to discover their causality. This paper is intended to list the basic operations to achieve the aims and to explore possible solving methods.

Methods, to some degree, means problem transformation. That is, a problem can be converted to another problem and then to be solved. This paper is developed to explore possible problem transformations surrounding the basic table decision operations.

The basic table decision operations can be converted to two different types of problems.

This paper provides formal description of the basic table decision operations and their transforming schemes so that strict mathematical proof can be used to guarantee their correctness.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.

The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.

The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.

The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.

The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems

This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

Because presentation formats, i.e. table v. graph, in shopping web sites may promote or inhibit deliberate consumer decision making, it is important to understand the effects of information presentation on deliberateness. This paper seeks to empirically test whether the table format enhances deliberate decision making, while the web map weakens the process. In addition, deliberateness can be influenced by the decision orientation, i.e. emotionally charged or accuracy oriented. Thus, the paper further examines the effect of presentations across these two decision orientations.

Objective and detailed description of the decision process is used to examine the effects. A two (decision orientation: positive emotion v. accuracy) by two (presentation: map v. table) eye‐tracking experiment is designed. Deliberateness is quantified with the information processing pattern summarized from eye movement data. Participants are required to make preferential choices from simple decision tasks.

The results confirm that the table strengthens while the map weakens deliberateness. In addition, this effect is mostly evident across the two decision orientations. An explorative factor analysis further reveals that there are two major attention distribution functions (global v. local) underlying the decision process.

Only simple decision tasks are used in the present study and therefore complex tasks should be introduced to examine the effects in the future.

For consumers, they should become aware that the table facilitates while the map diminishes deliberateness. For web businesses, they may try to strengthen the impulsivity in a web map filled with emotional stimuli.

This research is one of the first attempts to investigate the joint effects of presentations and decision orientations on decision deliberateness in the internet domain. The eye movement data are also valuable because previous studies seldom provided such detailed description of the decision process.

Managers are confronted with increasing information overload and growing pressure for effective and efficient decision making. The visualisation of data represents a way to overcome this dilemma and to improve management decision quality. The purpose of this paper is to transfer insights from visualisation research to the managerial accounting context and clarify the impact of visualisation on management accounting reports and decision making. The authors deduce implications for behavioural management accounting research, teaching, and business practice from previous findings and the results.

The authors conducted an experiment with students and experienced managers. Participants had to evaluate eight different business units based on four accounts (sales, EBIT, FPY, and delivery reliability). The information the authors provided to the participants was either presented as tables only, or in tables and graphs.

The empirical results show that supplementary graphs improve decision quality, especially within the manager sample but do not affect decision confidence in a performance evaluation task. The authors furthermore find that managers perform poorly when only provided with tables, and they achieve the overall best score when provided with both tables and graphs, whereas students perform similarly in both conditions. The authors additionally show that proficiency affects not only decision quality but also decision confidence.

The results differ from predictions based solely on the cognitive fit model, as the authors found differences in decision quality to be stronger within the group of managers. The cognitive fit model proposes that decision making performance will improve when the problem representation and the decision making task match. Applying the model to a management context, it is obviously insufficient to explain the differences the authors obtained in the experiment. The authors observed that proficiency plays a role in such performance evaluation tasks.

Based on the results, management accountants should analyse the task that needs to be solved with the reported data. By analysing the type of task, accountants can derive the information processing strategy that will most likely be used by executives for problem solving and determine the suitable visualisation format based on the cognitive fit model. Moderate or complex monitoring tasks will presumably be accessed with perceptual information processing. Data should thus be visualised with graphs.

The authors provide empirical evidence that supplementary graphs in management reports improve decision quality but not decision confidence. The authors furthermore illustrate the limits of the explaining power of the cognitive fit model in a management report context. In an extension of cognitive fit theory, the authors argue that proficiency plays a crucial role in performance evaluation tasks. The authors propose a process for visualisation of management reports based on their findings and previous findings.

This research attempts to explain the decentralization of investment decision. To do so, it highlights the role of the internal capital market in the allocation of decision rights and control as a factor explaining the effectiveness of investment management. The authors aim to apply the theory of the organizational architecture to the investment decision to understand its complexity and its efficiency.

An empirical test was realized on a sample of 63 Tunisian firms using the methods of canonical correlation and cross tabulations.

Even if organizational complexity has a linear and negative impact (opposite sign to what is expected) on the investment decision decentralization, which creates value, it appears that there is a positive association with the uncertainty of the environment, and a negative one with the scarcity and sharing of financial resources between units on the internal capital market.

The authors show that the role played by the internal capital market in the value creating requires the setting of a centralized organizational structure.

Selection of best product recovery alternative in reverse logistics (RL) has gained great attention in supply chain community. The purpose of this paper is to provide a robust group decision-making tool to select the best product recovery alternative.

In this paper, fuzzy values, assigned to various criteria and alternatives by a number of decision makers, are converted into crisp values and then aggregated scores are evaluated. After obtaining experts’ scores, objective and subjective weights of the criteria have been calculated using variance method and analytic hierarchy process, respectively. Then integrated weights of criteria are evaluated using different proportions of the two weights. The superiority and inferiority ranking (SIR) method is then employed to achieve the final ranking of alternatives. An example is presented to demonstrate the methodology.

The proposed methodology provides decision makers a systematic, flexible and realistic approach to effectively rank the product recovery alternatives in RL. The alternatives can easily be benchmarked and best recovery strategy can be obtained. The sensitivity analysis carried out by changing different proportion of objective and subjective weights reveals that best ranking alternative never changes and proves the robustness of the methodology. The present benchmarking framework can also be used by decision makers to simplify any problem which encounters multi-attribute decision making and multiple decision makers.

The proposed methodology should be tested in different situations having varied operational and environmental conditions dealing with different products. A real case study from an industrial set up can help to assess the behavior of the proposed methodology. The presented methodology however can deal with such multi-disciplinary and multi-criteria issues in a simple and structured manner and ease the managers to select the best alternative.

A novel approach for decision making taking into account both objective and subjective weights for criteria has been proposed to rank the best recovery alternatives in RL. The proposed methodology uses SIR method to prioritize the alternatives. As RL alternative selection is an important issue and involves both technical and managerial criteria as well as multiple decision makers, the proposed robust methodology can provide guidelines for the practicing managers.

In the present time, there are large databases with parameters related to the maintenance of different equipment and installations. Given that manual analysis of sensors connected to machines is practically impossible, maintenance decisions from these databases can be difficult if the information automatically updated from these sensors is huge. Those great amounts of information are essentially useless if the knowledge contained inside cannot be extracted. Rough set theory facilitates this work by detecting those parameters that are truly significant for establishing the decision rules of the maintenance. In order to show the power of rough sets this paper contains a real case of a plastic injection installation for the analysis. Practical implications. An effective use of resource allocation in manufacturing processes could be achieved by using certain decision rules to indicate where and when maintenance decisions and tasks should be undertaken. This paper illustrates how the powerful theory of rough sets handles these issues. Therefore, the use of this technique is highly recommended for those industrial processes with a great amount of data and time (or in general, any resource) limitations.

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.

This paper aims to develop an efficient measurement index evaluation system towards assessing suppliers' green performance practices. Apart from estimating overall performance index, the paper also aims to highlight application of decision‐support tools for selection of appropriate candidate supplier in green supply chain management context.

In order to tackle incompleteness and imprecision arising from assigning appropriateness rating as well as priority weights against subjective performance criteria‐attributes, use of grey numbers was proposed. An efficient grey‐based supplier appraisement platform was established. Application of Grey‐Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and COPRAS‐G method were reported to solve the supplier selection decision‐making problem. The theory of grey numbers was utilized here to facilitate such decision modeling.

Over the last two decades, growing concerns about ecosystem quality have stimulated to a renewed interest in environmentalism. Purchasing professionals should also be concerned and need to rethink purchasing strategies which have traditionally neglected environmental impacts. The “green” purchasing‐packaging in reducing and eliminating waste is a major concern in recent days. In order to help foster environmentally concerned purchasing strategies, this paper presents the findings of supplier evaluation strategy in an enterprise with enhanced degree of awareness and frequent applications of “green” purchasing. Environmental factors are identified that may reshape supplier evaluation decisions. The concept of grey numbers set has been adopted in this work. A case study reflects effectiveness of exploring grey relation theory in the context of green supplier evaluation.

The major contributions of this work have been summarized as follows: development and implementation of an efficient decision‐making tool to support green supplier evaluation; an overall green performance index evaluation platform has been introduced; concept of grey numbers has been efficiently explored to facilitate this decision‐making; the appraisement index system has been extended with the capability to search ill‐performing areas which require future progress; and the proposed appraisement system is capable of reducing the number of green attributes towards computing grey appropriateness index thereby transforming into lesser number of green capabilities, thus, facilitating applying decision‐making tools like grey‐TOPSIS and COPRAS‐grey method for appropriate supplier selection from a set of candidate suppliers.