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The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

The purpose of this paper is to focus on the adjustment of the GM(1, 2) errors for financial data series that measures changes in the public sector financial indicators, taking into account that the errors in grey models remain a key problem in reconstructing the original data series.

Adjusting the errors in grey models must follow some rules that most often cannot be determined based on the chaotic trends they register in reconstructing data series. In order to ensure the adjustment of these errors, for improving the robustness of GM(1, 2), was constructed an adaptive fuzzy controller which is based on two input variables and one output variable. The input variables in the adaptive fuzzy controller are: the absolute error ε i 0 ( k ) [ % ] of GM(1, 2), and the distance between two values x i 0 ( k ) [ % ] , while the output variable is the error adjustment A ε i 0 ( k ) [ % ] determined with the help of the above-mentioned input variables.

The adaptive fuzzy controller has the advantage that sets the values for error adjustments by the intensity (size) of the errors, in this way being possible to determine the value adjustments for each element of the reconstructed financial data series.

To ensure a robust process of planning the financial resources, the available financial data are used for long periods of time, in order to notice the trend of the financial indicators that need to be planned. In this context, the financial data series could be reconstituted using grey models that are based on sequences of financial data that best describe the status of the analyzed indicators and the status of the relevant factors of influence. In this context, the present study proposes the construction of a fuzzy adaptive controller that with the help of the output variable will ensure the error’s adjustment in the reconstituted data series with GM(1, 2).

The airline industry has been significantly hit by the occurrence of the new coronavirus SARS-CoV-2, facing one of its worst crises in history. In this context, the present paper analyses one of the well-known boarding methods used in practice by the airlines before and during the coronavirus outbreak, namely back-to-front and suggests which variations of this method to use when three passenger boarding groups are considered and a jet bridge connects the airport terminal with the airplane.

Based on the importance accorded by the airlines to operational performance, health risks, and passengers' comfort, the variations in three passenger groups back-to-front boarding are divided into three clusters using the grey clustering approach offered by the grey systems theory.

Having the clusters based on the selected metrics and considering the social distance among the passengers, airlines can better understand how the variations in back-to-front perform in the new conditions imposed by the novel coronavirus and choose the boarding approach that better fits its policy and goals.

The paper combines the advantages offered by grey clustering and agent-based modelling for offering to determine which are the best configurations that offer a reduced boarding time, while accounting for reduced passengers' health risk, measured through three indicators: aisle risk, seat risk and type-3 seat interferences and for an increased comfort for the passengers manifested through a continuous walking flow while boarding.

This study aims to analyze the major risk categories that could be encountered in hospitals and other medical facilities and attempts to determine which are more important from the patients’ perspective for the purpose of improving to the hospital–patient relationship improvement. For this, five main risk categories are identified along with an overall perceived risk.

To extract the patients’ opinion over the considered types of risks in terms of importance and exposure to these risks when using the medical services, a questionnaire has been created and validated using AMOS 22.0.0. Due to the validation process, a series of variables have been excluded, while the selected ones have been used for calculating the overall perceived risk. Having the values of this risk for the entire set of respondents (N = 304), the grey incidence analysis has been applied to determine whether there is a correlation between the overall perceived risk and the frequency of medical services usage, the disease gravity, the hospitalisation period or the healing degree.

The human resources and the hospital conditions risk has been mentioned as the main risk category encountered by the respondents when accessing the medical services both in term of importance and exposure, shortly followed by the technological and hospital conditions risk. The overall perceived risk has a moderate to high average value on the entire set of respondents and it is mostly related to the frequency to which the respondents are utilising the medical services.

In this paper, the hospital’s risks are analysed from the patients’ point of view to see both their perception over these risks and the importance they are giving to these risks. More, an overall perceived risk has been determined, with a moderate to high value on the Likert scale (on this data set), which can be useful if extended to each hospital (and not calculated as a general indicator), as it can provide a landmark for patients when choosing a hospital.

The article proposes a new method of strategic analysis. The method was called the grey portfolio analysis method. The presented method is complementary to the popular BCG matrix. The use of the grey portfolio analysis method enables to make a dynamic portfolio analysis for data with a high level of uncertainty.

First, the article presents current problems related to the application of portfolio methods in strategic management, in particular with reference to the BCG matrix. Second, the basics of grey numbers, operations with them and the way of acting in the grey portfolio analysis method are presented. Finally, the developed method is presented in a case study concerning an IT enterprise, whose portfolio includes cloud computing services.

In the article, a new method of a strategic analysis based on the BCG matrix was presented. It combines grey methodologies of decision making with a grey prognostic model in the context of a strategic analysis. Due to this fact, a dynamic approach to the issues of portfolio methods is possible.

The article fits the current need related to the development of new expert systems supporting strategic management in enterprises.

An introduced method is new and innovative in the area of portfolio methods. Its originality results from the fact that it eliminates a static nature of the BCG matrix through the use of grey prognostic models. What is more, when grey numbers are used, a problem of uncertainty of information, which appears, is solved at a methodological level.

As the grey systems theory has been used over the time in different economic areas, in the following, a short literature review will be put forward, starting from the usage of these theory in the supply chain management, decision-making process, financial performance evaluation, credit risk, energy consumption, investment efficiency, etc. The purpose of this paper is to identify some key studies from all the economic areas in which the grey systems can be used in order to open and to bring to the researchers new domains in which they can manifest their interest and in which they can successfully use the methods offered by the grey systems theory.

Using the search engine offered by the Web of Science, a literature review has been performed for the economic grey systems applications developed over the time on both economic diagnosis and system’s forecasting. In addition, some hybrid grey systems theory – artificial intelligence techniques models have also been presented.

The grey systems theory has brought its contribution to numerous economic application from various fields such as: supply chain management, decision-making process, financial performance evaluation, credit risk, energy consumption, investment efficiency, firms’ bankruptcy, product development, consumer income, monetization ratio, etc.

The present paper identifies the some of the most representative examples in which the grey theory has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.

Unlike other review papers written on the grey systems theory area, the present paper is only focusing on the economic applications in which this theory has been successfully used, bringing to the reader a general overview on this field and, in the same time, enabling new research perspectives.

The purpose of this paper is to synthesize the review of the existing literature attached to the grey economic system theory and applications and aims to offer a comprehensive picture of the contribution brought by the researchers to this particular field. Also, the paper underlines the main research areas within the grey economic theory and applications and serves as an informative summary kit for future research works and research directions.

For appreciating the scientific progress made since the grey systems theory has been initiated to the present, with an accent on the literature dedicated to the economic field, a bibliometrics analysis has been conducted. The Perish or Publish software was used for extracting the needed data from Google Scholar for the entire period since the appearance of grey systems to now-a-days. In addition, an ISI Web of Science (WoS) search has been performed for extracting the grey economic papers. As the main focus is on the economic subject area of the grey systems, only the papers related to this field have been selected.

The total number of grey economic paper from both Google Scholar and ISI WoS database, the number of authors, some citation metrics, H-index, authors’ provenience country, papers’ language, etc., have been presented and analysed. Also, a list with the most cited papers in the grey economic relational analysis, grey economic prediction models and grey economic incidence is putted forward.

Through the bibliometric analysis on grey economic papers written over time, a qualitative analysis was performed on this field in order to underline the main research direction, to analyse what has been done in this field and to determine which can be the next research directions that can emerge from here.

The paper succeeds in enlarging the view regarding the usage of grey systems theory in the economic field, offering a suitable analysis on the considered areas. Even though bibliometrics analysis have been conducted on the grey systems theory field, a grey economic bibliometric analysis has not been done yet, to the authors’ knowledge. Therefore, a synthesized of the existing literature attached to the grey economic system theory and applications is presented in order to offer a more comprehensive picture of the contribution brought by the researchers to this particular field.

The identification of the main risk triggers is essential for the hospital’s survival and performance with direct effects on its patients’ health and well-being. For this reason, in this paper some of the most important risk categories have been determined. While in a previous research a qualitative analysis has been done for determining which are the most important risks felt by the patient that are believed to affect their health through the usage of a questionnaire and through conducting a confirmatory factor analysis, the purpose of this paper is to analyze the quantitative side of these risks’ presence in a hospital.

On this purpose, four main categories of risks have been considered (the same as in the qualitative research) and they have been analyzed from the hospital’s point of view – through the usage of the hospital financial and internal documents. Therefore, a series of indicators have been determined for each risk category. After that, a representative indicator has been selected and the grey incidence analysis has been conducted.

By comparing the results gathered form this study with the qualitative analysis conducted among the patients (Delcea et al., 2016) it can be said that there can be seen a difference among the way a hospital and a patient perceive the risks within a medical activity. While for the hospital, the most affecting risk is the technological and hospital conditions risk, for the patients the most affecting risk seems to be the human resources and clinical risk. The mismanagement risk and inability to treat patients is the second in intensity for both the hospital and patients, with a smaller value in the patients’ case.

From here, the research can be extended for capturing the risks that are considered to be important for the medical stuff, which will permit us to have a global image over the healthcare risks. After that, a comparative analysis among the hospitals with different financial performance can be conducted in order to see how these risks are affecting their performance and to determine which can be the decisions that can fostering the reduction of these risks.

The present paper offers a quantitative analysis from the hospital’s point of view using the advantages offered by the grey systems theory. Combining this analysis with a qualitative one conducted on the patients, the managers of the hospital can a have a more adequate view over the risks that they are facing with. In this context, grey systems theory offers the needed methods for dealing with such situations.

Recent studies have shown that customers are more likely to buy a certain product or service from a company they can follow or contact on social media. Moreover, the customers feel a stronger relationship with the companies they are interacting in the virtual networks. But have the companies succeed in getting everything from this strong relationship? Are their online advertising campaigns getting to the right customers? Is there any connection between the social media engagement and the decisions users are taking? This is going to be shaped in this paper through a grey analysis applied to the selected variables. The paper aims to discuss these issues.

As the nature of the relationships created in the online social networks is characterized by incomplete information, the analysis will make used of the concepts and means offered by the grey systems theory, a theory that have obtained good results over the time when used on uncertain situations.

By applying a grey relational analysis (GRA) to the considered variables, a strong relationship between the decision easiness and both time spent on social media platforms and number of the accessed websites has been identified. Moreover, it has been determined that the decision happiness is closely related to the companies’ websites and their commercials.

The present paper shapes the relationship between the usage of online social media and consumers’ decision-making process and decision-making happiness. Due to the fact that the online social media includes billions of users worldwide, the study has some limitations due to the users’ number.

The paper uses GRA for drawing the connections between the online social networks reality and its influence to users’ every-day life. Considering the present findings, it can be underlined that the identification of the persons which are influential becomes important in order to get to the proper targeted customers.

Performance indicates how well the activities, operations and business processes of an enterprise are performed. In health care, performance illustrates the quality of medical services and targets the strategic objectives, the efficiency and effectiveness and the obtaining of the desired financial results. In economic analysis, the key performance indicators (KPIs) offers real, relevant and accurate information regarding the performance, using analysis and diagnosis techniques. The purpose of this paper is to discuss grey systems theory in order to bridge the KPIs and the hospital’s performance.

Using the grey systems theory, the impact of seven selected KPIs (the beds utilization rate, the average length of hospitalization, the average cost of hospitalization/day, the proportion of physicians in total staff, the nosocomial infection rate, the death rate and the diagnostic concordance) on the hospital’s turnover is determined.

By analyzing the grey incidence between turnover and several KPIs, for a period of six years, it has been concluded that the greatest impact on performance has the diagnostic concordance and the percent of the physicians in the total staff, followed by the nosocomial infection rate, the average bed utilization rate and the death rate.

The considered case study should be extended by including a greater number of hospitals into the analysis.

The usage of the grey systems theory in an uncertain and limited information field such as the medical one, is a premier in this field. According to the grey incidence analysis results, the performance of health care institutions depends mainly on the quality of medical staff (that influence the diagnostic concordance, the nosocomial infection rate and the death rate) and on the management ability to attract competent and well-trained physicians, which can conduct in a new approach that should be considered by the hospitals’ managerial staff.