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The chapter aims to investigate the impact of the COVID-19 pandemic and crisis on the implementation of the game market strategy of clustering business structures. The chapter contributes to the literature by clarifying the concept of economic clustering from the perspectives of game theory and stakeholder theory in the COVID-19 pandemic and crisis. The scientific novelty and originality of the research results are that they revealed differences in the effectiveness of the game strategy of clustering business structures, first, between developed and developing countries and, second, between conditions of stability and conditions of crisis. The theoretical significance of the results and conclusions is that they opened a new perspective on the clustering of business structures – from the perspective of game theory (as a game strategy in its alternativity with the strategy of individual business presence in the market) and from the perspective of stakeholder theory (as a market strategy, the effectiveness of which is evaluated for all stakeholders). The practical significance of the research lies in the fact that it allows rationalising the decision-making on the implementation of the game strategy of clustering business structures in the context of the COVID-19 pandemic and crisis, considering the peculiarities of developed and developing countries. The authors provide their recommendations for each category of country.

Multiple linear regression (MLR) is a commonly used statistical technique to predict future values. In this paper, we examine the situation in which a given time series dataset contains numerous observations of important predictor variables that can effectively be classified into groups based on their values. In such situations, cluster analysis is often employed to improve the MLR models predictive accuracy, usually by creating separate regressions for each cluster. We introduce a novel approach in which we use the clusters and cluster centroids as input data for the predictor variables to improve the predictive accuracy of the MLR model. We illustrate and test this approach with a real dataset on fleet maintenance.

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

This study examines associations between accrual earnings management (AEM) and real earnings management (REM), and earnings quality between countries considered under insider economics and outsider economics clusters. Countries included in the outsider economics cluster are Singapore, Malaysia, and Hong Kong. Meanwhile, countries included in the insider economics cluster are Indonesia, the Philippines, and South Korea. Earnings management practices have changed from AEM to REM since the publication of the Sarbanes Oxley Act and DFA 954 implementation of the Claws back provision policy in the United States.

Research data were obtained from the Bloomberg database, 2010–2016. Regression analysis and t-test were utilized. This study compared AEM and REM to determine which is stronger based on country clusters, as well as the association between AEM or REM and earnings quality.

The results of this study indicate that AEM and REM are associated with the quality of earnings in the insider economics cluster. However, AEM and REM are not associated with earnings quality in the outsider economics cluster. Furthermore, associations between AEM and earnings quality are stronger than associations between REM and earnings quality in insider economics cluster.

This research is to determine scenarios and perspectives for improving the cluster strategy of business integration in the post-COVID-19 era with the help of the methodology of the game theory.

The methodology of this research includes the complex method, statistical method, correlation analysis and the game theory of decision-making.

Based on the analysis of scientific approaches, we formulate the authors' treatment of the essence of the notion of clustering, which characteristics are evaluated in this work. In this treatment, we distinguish factors that influence the development of clustering of business structures of the state, which level is assessed within the analysis. The components of the competitiveness of business structures are among such factors. Cluster structures of certain countries successfully functioned during the COVID-19 pandemic, using effective strategies created independently (United States) and based on the strategies of non-market regulation (China).

The scientific novelty of this research consists in the identification of the types and characteristics of the strategies of clustering of business structures formed during the COVID-19 and post-COVID-19 eras.

To determine the influence of the development of clustering of the national business environment on the level of digital competitiveness.

The research was performed using the following methods: statistical analysis, correlation analysis and comparative analysis.

We study the influence of the development of clustering of the national business environment on the level of digital competitiveness. It is revealed that the studied developed countries (Singapore, Denmark and Switzerland) demonstrate a high level of clustering of business, which is assessed through the use of the indicator ‘State of сluster development’, and a high level of digital competitiveness. The considered developing countries (Peru, Mexico and the Philippines) have medium values of the above variables. Only Peru was able to use a highly effective mechanism of clustering, which influenced the digitalisation of sectors of the economy, which have business clusters. We also describe the competitive advantages of the development of cluster entrepreneurial structures, which ensure their economic and market success.

The scientific novelty of the results obtained is due to the elaboration on the specifics of the influence of the cluster strategy of business integration on the level of national digital competitiveness.

Financial ratios are often used to classify firms into different clusters of financial performance. This study aims to classify firms using financial ratios with advanced techniques and identify the transition matrix of firms moving clusters during the COVID-19 period.

This study uses compositional data (CoDa) analysis based on existing clustering methods with transformed data by weighted logarithms of financial ratios. The data include 66 listed firms in Vietnam’s food and beverage and fishery sectors over a three-year period from 2019 to 2021, including the COVID-19 period.

These firms can be classified into three clusters of distinctive characteristics, which can serve as benchmarks for solvency and profitability. The results also show the migration from one cluster to another during the COVID-19 pandemic, allowing for the calculation of the transition probability or the transition matrix.

The findings indicate three distinct clusters (good, average and below-average firm performance) that can help financial analysts, accountants, investors and other strategic decision-makers in making informed choices.

Clustering firms with their financial ratios often suffer from various limitations, such as ratio choices, skewed distributions, outliers and redundancy. This study is motivated by a weighted CoDa approach that addresses these issues. This method can be extended to classify firms in multiple sectors or other emerging markets.

The study aims to provide a reference for market segmentation in a relatively new market. Esports consumer profiles are developed based on consumption motives, structural factors, game genres, interests, demographics and behavioral intentions. It delivers managerial advice for a growing esports market.

A quantitative approach using an online survey was implemented to identify homogenous groups. The study employed the Motivation Scale for Sports Consumption (MSSC) to investigate the consumption motives of esports consumers. A two-step market segmentation was conducted based on the motives, applying hierarchical clustering. Moreover, descriptor variables were used to create distinct esports consumer profiles.

This research divides the esports market into four clusters based on MSSC, which is new and relevant in a constantly changing environment. The clusters are named Low Intention Novices, Leisure Warriors, Socializing Learners and Dedicated Enthusiasts.

This adds to the limited literature on esports market segmentation and highlights the theoretical and practical implications of the findings.

The aim of this study was to empirically examine how much traditional attributes and green attributes characterize products within design-oriented firms. Further, we explored how these attributes relate to the perceived level of innovation of the firms.

An exploratory research was carried out in 86 Italian manufacturing companies that are members of the Industrial Design Association. Using the questionnaire method, the entrepreneurs’ perceptions have been analyzed. Data have been treated with hierarchical cluster analysis.

The analysis shows that environmental sustainability is the least important attribute of a design product and four clusters of highly design-oriented firms differ by design-product attributes. Further, the least green firms are also the least innovative in terms of incremental and general innovation.

The small size of the sample and the provenance of firms from a single country imply limited generalizability, and further research on the topic is recommended.

Design-driven innovation based on traditional design attributes provides many competitive advantages to firms. However, given the growing concern about environmental challenges, investing in green attributes in design products allows for remaining competitive and more effective in innovation.

This study, for the first time, reveals the heterogeneity among design-oriented firms, particularly regarding the presence and assortment of traditional design attributes, as well as the incorporation of environmentally friendly attributes in their products. Moreover, the study uncovers the relationship between varying levels of green attributes in the offerings and the perception of the firm’s innovativeness.