One of the key areas of contemporary marketing is the formulation of a pricing strategy, which is one of the four pillars of the traditional marketing mix. One way to implement this strategy is through dynamic pricing. It is currently gaining popularity in many industries for two reasons. Firstly, it is possible, easy, and cheap to collect information about transactions and customers. Secondly, machine learning mechanisms, for which these data are essential, are becoming widely available. The aim of this article is to propose a dynamic pricing method for the e-commerce industry. To achieve this goal, machine learning methods such as the Naive Bayes classifier, support vector machines (linear and nonlinear), decision trees, and the k-nearest neighbor algorithm were used. The empirical results indicate that the linear support vector machine achieved the highest accuracy (86.92%), demonstrating the model’s effectiveness in classifying pricing decisions. This article aligns with two leading research trends in dynamic pricing: personalized dynamic pricing (the target model considers customer-related criteria) and the development of systems to assist managers in optimizing pricing strategies to increase revenues (using machine learning methods). This article presents a literature review on dynamic pricing and then discusses the machine learning methods applied. In the final part of this article, verification of the developed dynamic pricing method using real-world conditions is presented.

Purpose Considering current development of Grey Systems Theory (GST), we can come up with the following thesis: practical applications are a dominant subject of research. Thus, what seems to be symptomatic for relatively young knowledge disciplines, the authors observe the presence of imbalance between the development of GST application tools and theory's epistemological and methodological background. As for GST, epistemological and methodological problems are becoming visible especially in the issues of determining a clear criterion of demarcation of this kind of a theory from others. In other words, this problem can be reduced to the issue of a precise determination of what the category of a grey system and grey information is. This problem is of great importance for further development and popularisation of GST in the world of science. Realising its significance, the purpose of this paper is to create a general overview of Grey Systems epistemology and afterwards create axiomatic and formal frames for a category of greyness. Design/methodology/approach In order to achieve set goals, two research approaches were accepted. In the area of inference about epistemology of GST an approach characteristic of an analytical philosophy was used, whereas in the case of axiomatic and formal frames for a category of greyness the authors referred to terms of a set theory and the principles of a pragmatic logic. Findings The result of research is to formulate a concept of a grey system and a concept of grey information in the context of a process of cognition. Moreover, a function of greyness and other fundamental categories of GST will be defined in an axiomatic way. Originality/value The paper presents a new consistent frame for the issues of methodological and epistemological backgrounds of GST. An original concept is to refer in considerations to a newly proposed grey space. This space was used for a formal justification of such elementary categories as grey numbers, a weight function of whitenization or grey sequences. The value of achievements shown in the paper is underlined by the fact that proposed theoretical constructions require further development and they can potentially open up new research trends in the GST.

The article proposes a new method of occupational risk assessment which uses the grey decision model. The method is called the grey occupational risk assessment model (GORAM). The presented method allows for combining measurable and qualitative factors in the process of occupational risk assessment through the use of expert knowledge. This method can be applied to the multifaceted occupational risk assessment of complex and uncertain sociotechnical systems. First, the article presents problems with currently used methods of occupational risk assessment in the context of the possibility of solving them using the methods of the grey system theory. Secondly, the algorithm of the GORAM method is presented. Finally, the developed GORAM model was used in a case study involving the position of a mechanic in the technical department of a production company.

In the paper, a new indicator exemplifying the conversion efficiency of expenditures towards economic growth into results pertaining to sustainable development, dubbed the “Synthetic Efficiency Indicator for Economic Growth” (hereinafter: “SEI-EG”) has been proposed. The inspiration for proposing such an indicator was the identification of the lack of connections between research on economic convergence and the research area connected with sustainable growth category. It was assumed that, in the first place, outcomes of the proposed convergence will be visible in developed economies, represented by EU15 member states. The set goal was to provide an answer to the question of difference between EU15 member states with respect to efficiency of converging expenditures exemplifying economic growth into results pertaining to sustainable growth. The research was conducted for 2016–2018 using Grey System Theory. With the use of the elaborated indicator, the authors created a ranking list of countries based on the efficiency of economic growth towards sustainable growth criterion. The conducted research proved that, in general, the smaller EU member states are characterized by significantly higher efficiency of converging expenditures exemplifying economic growth into results pertaining to sustainable development in the researched area. Among the countries with large economies, only Germany showed efficiency comparable to smaller ones.

This article builds upon the authors’ previous work on the Synthetic Efficiency Indicator for Economic Growth (SEI-EG), demonstrating the process of transforming economic-growth-related inputs into sustainable development outcomes. This innovative application of the SEI-EG provides a fresh perspective on the effects of eurozone membership on the sustainability efficiency of EU countries, thereby enriching the discourse on economic integration and sustainability efforts within the European Union. By integrating the economic dynamics of the euro area with environmental efficiency metrics, this study offers novel insights into the potential influence of currency union membership on achieving sustainable development goals. Covering the entire European Union, categorized by euro area and non-euro area membership, this study navigates through the risks to sustainability posed by global crises and the ongoing debate over the euro’s integration success and setbacks. Conducted from 2019 to 2021 using grey system theory, this research incorporates a revised set of seven indicators in the domain of industry, innovation, and infrastructure as recommended by the Europe 2020 project. The findings confirm the initial hypothesis that countries outside the euro area tend to exhibit higher efficiency as measured by the SEI-EG indicator. This article is composed of five parts. The first two parts present characteristic features of economies in the euro area and non-euro area, along with a critical trend in the latest literature on the benefits and risks of economic integration. The subsequent sections introduce the methodology for determining the indicator and the authors’ own corrections to it as well as the results of the research and a discussion.

There is an increasing demand for robust methodologies to rigorously evaluate the psychometric properties of measurement scales used in quantitative research across various scientific disciplines. This article proposes an integrative method that combines structural equation modelling (SEM) with machine learning (ML) to jointly assess model fit and predictive accuracy, limitations often addressed separately in traditional approaches. Using a measurement scale for voluntary employee turnover intention, the method demonstrates clear improvements: RMSEA decreased from 0.073 to 0.065, and classifier accuracy slightly increased from 0.862 to 0.863 after removing three redundant items. Compared to standalone SEM or ML, the integrated framework yields a shorter, better-fitting scale without compromising predictive power. For practitioners, this method enables the creation of more efficient, theoretically grounded, and predictive tools, facilitating faster and more accurate assessments in organisational settings. To this end, this study employs Covariance-Based SEM (CB-SEM) in conjunction with classifiers such as naive Bayes, linear and nonlinear support vector machines, decision trees, k-nearest neighbours, and logistic regression.