Synergy of Modern Analytics and Innovative Managerial Decision-Making in the Turbulent and Uncertain New Normal

Abstract

This paper focuses on analyzing the relationship between the financial performance of companies and their ability to utilize modern business methods. Financial analysis was conducted using the example of the automobile manufacturer Škoda Auto, with the results providing deeper insights into the company’s financial situation. The companies examined in this study were scored and underwent regression and cluster analyses. A questionnaire focusing on the modernity of advertising in selected companies was answered by 276 respondents. Based on the findings, a model for evaluating the modernity and stability of companies was developed, combining various factors including financial indicators and the adoption of modern technologies. The results indicate that there is a relationship between financial performance and the modernization of companies, although this relationship is not always straightforward. In particular, the operating profit and current ratio emerged as important factors influencing modernization. Overall, it can be concluded that the financial performance and modernization of companies are interconnected, but their relationship is complex and requires further investigation. This paper is an important contribution to understanding company modernization and sets the stage for further studies on this issue.

1. Introduction

In today’s rapidly changing and competitive business environment, it is crucial for companies to successfully adapt to and utilize modern analytical tools. Within this context, cluster and regression analyses and neural networks are promising approaches to business analysis and managerial decision-making, enabling a deeper understanding of business processes and strategies. Researchers and managers in this field strive to connect these methods and create synergy, providing a comprehensive view of business data and supporting informed decision-making.

Cluster analysis, as a tool for grouping similar elements, has the potential to uncover new connections in business analysis. For instance, it can identify groups of customers with common characteristics, facilitating targeted marketing strategies; thus, incorporating cluster analysis is considered very important. In the realm of modern data analysis approaches, neural networks hold significant importance. Inspired by the structure of the human brain, these networks can process large amounts of data and identify complex patterns. According to a study in [1], neural networks have a wide range of applications in machine learning and can serve as an effective tool for classification and prediction. In the context of managerial decision-making, their ability to process complex information can provide deeper insights into business trends and forecasts of future events. Although both methods have been explored in various contexts, their synergistic potential in business analysis remains underestimated. By combining cluster analysis and neural networks, we can achieve a more comprehensive understanding of business data and a better informational basis for managerial decision-making.

AI and big data significantly impact modern businesses. Big data contains vast amounts of diverse information; machines, tools, and AI-supported devices help modern businesses process it quickly, efficiently, and meaningfully. Therefore, top executives and entrepreneurs must focus acutely on ontological and communicative perspectives to tackle various challenges and problems, especially in light of recent crises caused by the COVID-19 pandemic. Several authors have emphasized the importance of utilizing modern analytical tools in the business environment. According to [2], older analytical methods are often prone to errors and may be ineffective in addressing dynamic business challenges. Smith further argues that combining different analytical approaches can provide a more comprehensive view of business data and stimulate innovations in managerial decision-making.

According to [3], a key aspect of modern management is the ability to respond quickly to changes and uncertainties in the business environment. Cluster analysis and neural networks have become essential tools in achieving this agility, highlighting the need for flexible methods that can better respond to new stimuli in the environment and bring a fresh perspective to strategic management. Cluster analysis, originally developed for statistical analysis, is starting to play a crucial role in business management. The authors of [4] indicate that cluster analysis can effectively identify trends and patterns in business data, which is essential for strategic planning and decision-making.

In the realm of neural networks, research in [5] serves as an important indicator of the growing role of these technologies in managerial decision-making. Their ability to adapt to changing conditions and learn from new data is key in today’s rapidly evolving business environment. Neural networks can provide a deeper understanding of customer behavior and predict market trends. New insights into the synergistic use of cluster analysis and neural networks were addressed in a study emphasizing that the combination of these approaches can significantly enhance businesses’ ability to comprehend complex patterns in data and respond more effectively to changing business environments. Another critical aspect of modern managerial decision-making is the rational utilization of modern technologies and analytical tools. The authors of [6] highlight the aging of traditional analytical methods and the need for a transition to more innovative approaches. The authors of [7] suggest that integrating different analytical approaches can provide a more comprehensive view of business data and stimulate innovation in managerial decision-making. The main goal of this article is to construct a model for assessing the stability and modernization of a business through an in-depth analysis of the causal relationships between its financial performance and its ability to utilize modern business methods.

In this study, the following research questions were established:

• RQ1: Which Czech companies focus the most on modernization and modern advertising?
• RQ2: To what extent is the top-rated company in terms of modernization financially stable?
• RQ3: What AI tools does the top-rated company in terms of modernization use to improve business quality?
• RQ4: What is the relationship between the financial performance of companies and their ability to utilize modern business methods, such as social media, chatbots, and modern advertising?

2. Literature Review

2.1. Current State in Business Analysis

In today’s dynamic and competitive business landscape, business analysis plays a crucial role in strategic decision-making and business management. As stated in [1], the significance of this analysis lies in its ability to provide key information that enables businesses to flexibly respond to changes and adapt their strategies in line with the evolving environment. Businesses vary in their ability to adapt to environmental changes [8]. The business environment is characterized by rapid changes in trends, technology, and consumer preferences. Business analysis serves as a gateway to understanding these changes and enables businesses to be more flexible and agile in their strategic decisions. Without proper analysis, businesses could overlook key signals and miss out on opportunities or threats. Technologies can be combined for better analysis, and the authors of [4] emphasize the importance of integrating modern technologies into business analysis processes. Artificial intelligence and big data analytics provide businesses with tools for a deeper and more sophisticated understanding of data [9].

2.2. New Trends in Business Analysis

With the advent of the digital transformation and Industry 4.0, approaches to business analysis are changing [10]. The research in [11] has significantly contributed to the field of strategic management by introducing the concept of competitive analysis, and this impact is still ongoing. The authors of [3] present the concept of Industry Intelligence, which combines traditional analysis with industrial data, allowing businesses to better understand specific industries and leverage partnerships within the overall business ecosystem. Another significant trend is the integration of environmental and social analysis into business processes. The authors of [12] emphasize the importance of the “Triple Bottom Line”, where social and environmental responsibilities are considered alongside economic factors. According to [5,13], an increasing number of companies are focusing on social responsibility and sustainability, even in decision-making based on business process analysis. These trends may be a key factor in shaping business strategies. Industry Intelligence enables faster and more accurate responses to industrial changes, which can enhance the competitiveness of businesses [7]. The integration of environmental and social analysis contributes to the formation of more responsible and sustainable business models [5]. In this way, new trends in business analysis not only bring challenges but can also be a catalyst for achieving long-term sustainability and social relevance for businesses. The above mentioned is confirmed by Figure 1 respresenting bibliographic analysis of business analysis in years 2020-2024 provided in VOS Viewer platform.

2.3. Challenges in Current Business Analyses

Despite the many advantages of current business analysis methods, they also face challenges. The authors of [6,14] identify the need for efficient processing and interpretation of large amounts of data. They also emphasize the importance of cybersecurity to ensure the integrity and confidentiality of data in business analyses. With the increasing availability of data and the proliferation of analytical tools, there is the added challenge of effectively processing and interpreting vast amounts of information. The authors of [15] highlight the need for automated processes and the development of advanced algorithms for extracting relevant insights from extensive datasets, and the authors of [16,17] also discuss challenges associated with the growing volume of data and emphasize the necessity of data engineering and optimizing data access for efficient analysis [18,19].

• Business Resilience and Adaptability Analysis

As the pandemic progressed, business resilience analysis became crucial. Organizations were forced to evaluate their ability to survive and recover in uncertain environments. The study in [20] emphasizes the importance of systematic analysis of business adaptability, which includes the ability to respond quickly to changes, restructure processes, and maintain flexibility in the face of unforeseen events [21,22].

• Combining Predictive Analytics with Resilience Analysis

In practice, the challenge lies in combining predictive analytics with resilience analysis. Integrating these two approaches allows businesses not only to predict future events but also to respond effectively to unexpected situations. In [2], a new concept of “adaptive prediction” is presented, which combines forecasting with rapid adaptation to new circumstances.

The COVID-19 pandemic has significantly influenced approaches to business analysis, emphasizing the importance of agile and adaptable strategies. In the following sections, we will explore how organizations are responding to this new reality and how they are changing their business analysis strategies.

2.4. Strategic Decision-Making and Business Analysis

Business analysis plays a key role in the managerial decision-making process, especially in the formulation and implementation of strategies. This section outlines the importance of informed decisions based on data analysis [23]. Business analysis provides managers not only with data but also with the ability to identify key factors affecting the organization’s strategic goals. This opens the space for a deeper understanding of the environment in which the business operates, enabling better strategy formation.

Managerial decisions regarding strategies require an informed approach supported by business analysis. Using the data from business analysis can minimize the uncertainty and risks associated with strategy formulation, ultimately leading to better strategies and strengthening the business’s competitive position in the market [4]. Risk analysis enabled by business analysis is key to preventing the undesirable impacts of strategies. Identifying and assessing risks allow managers to better respond to changing conditions and minimize potential negative impacts on the business [24].

It can be said that the insights gained from business analysis lead to the formulation of better strategies. Managers can better understand customer needs, respond to changing market conditions, and create innovative strategies, which increases a business’s competitive advantage [4,25]. Integrating data analysis into the decision-making process supports synergies between analytical results and the business’s strategic goals. This integration provides a holistic view of strategic decisions, increasing their effectiveness and efficiency [2].

Overall, it can be concluded that informed decisions based on business analysis are a key factor for success in modern businesses. Management experts have emphasized the importance of data analysis for creating effective strategies and maintaining a competitive advantage in the market [26].

2.5. Cluster Analysis for Deeper Strategic Insights

Cluster analysis is a key tool in strategic analysis, allowing businesses to gain deeper insights into patterns and relationships among various factors affecting their performance and market position. This methodology uses statistical principles to group similar units or phenomena into clusters based on similarity, enabling the identification of patterns and trends in data.

The principles of cluster analysis are based on algorithms that identify similar elements in a dataset and group them based on similarity. This analysis can be applied to various data from demographic information to customer behavior or business performance indicators.

One key use of cluster analysis is market segmentation. Identifying groups of customers with similar characteristics allows for better-targeted marketing strategies and offers [27]. The optimization of internal processes [18] and competitive analysis [11] are other areas for which cluster analysis provides valuable information.

Performing cluster analysis on customer data, for example, can lead to the identification of groups with similar behaviors and preferences, enabling personalized marketing campaigns. Despite the advantages of cluster analysis, such as identifying hidden patterns and better understanding data, it is important to be aware of the limitations of this method, such as sensitivity to the choice of algorithms and initialization conditions [28]. Overall, it can be stated that cluster analysis is a powerful tool with which to obtain deeper strategic insights and achieve more effective decision-making in business strategies.

• The Importance of Relevant Data in Strategic Processes

Business analysis is an essential element in managerial decision-making, especially in the process of strategy formulation and implementation. Obtaining relevant data about the market, competition, and internal business processes plays a crucial role in this context [7].

• Market Data for Strategic Orientation

Obtaining relevant market data is a cornerstone for effective strategy formulation. Information about market size, trends, customer preferences, and potential competitors is invaluable when planning a business’s long-term goals. The authors of [1] emphasize that market data analysis enables managers to identify opportunities and create strategies that reflect current market needs [29].

• Competitive Analysis for Differentiation

Understanding the competitive environment is key to successful strategic decision-making. Obtaining data about competitors and their strategies, strengths, and weaknesses allows managers to differentiate their products and services in the market. Analyzing the competitive environment creates opportunities for innovation and improvement, contributing to the business’s long-term sustainability [30].

• Internal Processes for Effective Management

Internal business processes are a key element of strategic analysis. Obtaining data on the performance, efficiency, and effectiveness of internal processes allows managers to identify areas for improvement and optimization. According to [1], internal process analysis is essential for effective resource allocation and achieving strategic goals.

• Deeper Insights into the Environment for Informed Decision-Making

Obtaining relevant data not only provides information for strategic decisions but also allows managers to gain deeper insights into the business’s external and internal environments. This holistic view enables a better understanding of the relationships between various factors affecting business decision-making and the creation of strategies that are robust and adaptable to changes.

In conclusion, the importance of relevant data in strategic processes lies in the managers’ ability to obtain comprehensive and up-to-date information, serving as the basis for creating informed and successful strategies. In-depth analysis of these data leads to a better understanding of the surrounding environment and provides the business with a competitive advantage in a dynamic market [24,31].

2.6. Identifying Key Factors Affecting Strategic Goals

Business analysis not only provides data but also plays a key role in identifying factors that impact the organization’s strategic goals. This identification is a critical step in strategy formulation, allowing managers to better understand the environment in which their business operates, thereby contributing to the quality of strategy formation [32].

One of the key elements of business analysis is the monitoring and analysis of market trends. This includes assessing changes in demand, customer preferences, new technologies, and general market directions. Identifying these trends allows managers to adjust their strategies to reflect current and future market needs [33].

Competitor behavior is another important factor influencing a business’s strategic goals. Business analysis enables tracking competitors’ strategies, their market actions, and responses to changes in the external environment. Identifying competitors’ strengths and weaknesses allows managers to develop strategies that strengthen the business’s market position.

2.7. Analysis of Business Data—Multidimensional Data Analysis Methods

Clustering is a way to distinguish objects of interest into groups based on certain criteria (e.g., similarity and difference), and it plays an important role in managerial and decision-making processes. In business contexts, clustering is widely used in formulating various grouped entities, ranging from entities such as market segments, product categories, customer accounts, and competitive alliances to their activities such as behavioral patterns, performance levels, workflow types, and business models. It can also relate to other aspects such as social circles, cultural differences, societal values, and personalities. In governmental and political contexts, clustering also pertains to various areas such as nations, systems, economies, structures, media, and public policies [34].

In the era of big data, not only is traditional business becoming digitized, but innovative and rapid methods are also emerging for new data-driven businesses; data technologies and business analytics thus have become key competencies for managers and decision-makers in managing their organizations and leveraging competitive advantages. In this regard, effective data handling has become a critical success factor [35]. Clustering analysis has been and is considered one of the most relevant and useful techniques for formulating groups and supporting managerial decisions.

One way to effectively work with large data patterns is to use and, if necessary, develop suitable statistical methods. Multidimensional data analysis methods, applicable to datasets, can generally be divided into two main groups. The first group consists of methods intended for classifying units, and the second group consists of methods used to analyze relationships between variables.

Selecting a suitable method primarily depends on the type of problem being addressed. In the case of classification problems, where the goal is to classify data based on certain conditions and requirements, we can utilize two sets of classification methods: either cluster analysis or discriminant analysis, and factor analysis. Categories of clustering algorithms are shonw in

Table 1. Categories of clustering algorithms.

Based On	Algorithm
Clustering algorithm based on partition	K-means, K-medoids, PAM, CLARA, CLARANS
Clustering algorithm based on hierarchy	BIRCH, CURE, ROCK, Chameleon
Clustering algorithm based on fuzzy theory	FCM, FCS, MM
Clustering algorithm based on distribution	DBCLASD, GMM
Clustering algorithm based on density	DBSCAN, OPTICS, Mean-shift
Clustering algorithm based on graph theory	CLICK, MST
Clustering algorithm based on grid	STING, CLIQUE
Clustering algorithm based on fractal theory	FC
Clustering algorithm based on model	COBWEB, GMM, SOM, ART

Source: Author.

.

2.8. The Role of Cluster Analysis in Various Fields

Cluster analysis plays a crucial role in a wide range of areas, including psychology, biology, computer science, marketing, and business management. Its primary benefit is its ability to group data into clusters that can assist in decision-making processes, making it a valuable tool for marketing and business domains. The authors of [19] note that clustering approaches traditionally focused solely on purely numerical or categorical data. An important area of cluster analysis deals with mixed data, composed of both numerical and categorical attributes. Clustering mixed data is not simple, as there is a significant difference between similarity metrics for these two types of data.

In this context, Ref. [36] provides some technical details on the types of distances that can be used with mixed data types. It is important to emphasize that, in most applications of cluster analysis, practitioners focus either on numerical or categorical variables. The authors of [37] support the widely accepted view that clustering plays a vital role in management and decision-making processes.

2.9. Applications of Cluster Analysis Across Fields

This section highlights various application areas where cluster analysis has been utilized, such as computer science, bioinformatics, pattern recognition, data analysis, image processing, and market segmentation [38]. In combating crime, cluster analysis is useful for detecting credit card fraud or monitoring criminal activity in e-commerce. Unusual credit card transactions, such as very expensive and/or frequent purchases, could indicate fraudulent activities.

In biology, it can be used to identify taxonomies (hierarchical clustering) of plants and animals, categorize genes with similar functions, and analyze traits of different populations.

In insurance, customer portfolio segmentation can support targeted marketing activities, identify groups of car insurers characterized by the same policy, classify policyholders based on their perceived risk, and model insurance claim costs within each of these risk groups [39].

Clustering can also help classify documents on the internet and analyze weblog data to identify groups characterized by common access and navigation profiles.

In urban studies, cluster analysis can help identify geographical areas with similar uses within a spatial database through the analysis of territory observation databases.

In marketing, clustering can help identify different customer groups based on their purchases, aiding marketing managers in identifying various segments in the customer database and using this knowledge to develop targeted campaigns.

In seismic studies, clustering earthquake epicenters are used to recognize common patterns among the tested items.

2.10. Clustering vs. Classification: Unsupervised vs. Supervised Learning

Clustering (unsupervised learning) involves partitioning data into meaningful groups [40], whereas classification (supervised learning) aims to assign specific objects to these predefined groups. The difference is that in supervised learning, classes are predefined, and the information is used to classify future observations. In unsupervised learning, there is no preassigned classification system to indicate which observation belongs to which class [41].

The authors of [42] highlight that the main research tasks in cluster analysis over recent decades include clustering large datasets, clustering datasets with categorical variables, fuzzy clustering, and other techniques expressing uncertainty. Some related problems include dealing with outliers, determining the number of clusters, etc. Although these tasks were addressed early in the development of cluster analysis, interest in these methods grew at the turn of the 21st century with the development of data mining techniques. New algorithms for cluster analysis are being proposed not only by statisticians but also by computer scientists [43,44].

Similarity is the basis of cluster definition. There are many ways to quantify it, depending on the nature of the examined variables and the ultimate goals. Clustering algorithms are divided into hierarchical and non-hierarchical methods [45].

2.11. Hierarchical Clustering Methods

In hierarchical methods, a tree structure is developed for clustering observations; the leaves of the tree correspond to observations, and the nodes correspond to subgroups of observations. There is a hierarchy within the subsets associated with the tree branches.

There are two broad families of hierarchical methods: agglomerative and divisive methods. Agglomerative methods start with n groups, where each observation represents a group. Then, groups with low dissimilarity merge until k = 1, meaning all observations belong to the same group.

Divisive methods instead start with a single group and then recursively divide the groups until there are n groups. Both approaches take a dissimilarity matrix as input. The main characteristic of these methods is that once two groups are merged, they cannot be separated again, and similarly, once two groups are separated, they cannot become part of the same cluster again. Moreover, when using this type of algorithm, the same tree is used for all k values, each time referring to a different level of the tree. It has a fixed structure [46].

3. Data and Methods

For the research part of this study, data were collected from the Justice.cz portal, which provides access to companies’ financial statements. These data come from the Czech Republic and were selected based on the turnover of the companies. We will focus on several key indicators, including turnover and financial results, to gain a comprehensive understanding of the financial health of companies. Another layer of data will be the numbers of likes on the companies’ pages on the social network Facebook. Analysis of this social media data will allow us to assess the public perception of and engagement with these companies on this platform. The existence of chatbots on company websites will be examined as an indicator of modern approaches to online communication and customer interactions. Identification and analysis of chatbot functions will provide information on the companies’ levels of automation and technological innovation. The latest activity on social media, including the date of the last post, will be monitored to understand the regularity and relevance of the content that companies share on social media.

This will help us to evaluate their content strategy and interactions with the online community. An interesting dimension of the analysis is the assessment of the modernity of companies’ websites. This factor will be examined in the context of design, responsiveness, and technological features that the websites offer. Given the increasing importance of online presence in business, a modern and functional website is key. Companies with the highest turnovers among the top 10,320 companies in the Czech Republic, according to data from Justice.cz, will be selected for analysis. From this group, companies that have achieved significant success in the new post-COVID-19 era will also be selected to account for current trends and developments in the market. A total of 45 companies will be selected for which specific research methods will be applied.

This mixed sampling method will lead to a representative and diverse set of data for analyzing the relationships between companies’ financial performance and their modern approach with regard to social media, websites, and chatbots. A model will be gradually built, which will calculate the modernity of the company. This will be ensured by regression analysis, which will show whether there is a relationship between financial ratios and the modernity of the company. This will result in ratio indicators that the company needs to focus on to increase its modernity. In the next section, after entering financial data, the companies will be classified into groups of stable, moderately stable, or unstable companies using cluster analysis. The company will be informed as to which cluster it belongs to and which indicators it should focus on to ensure stability. Due to the necessity of manual data retrieval regarding modernity, the sample size for modernization point calculation was reduced to 45 companies. For the analysis, 45 companies were selected that represent the key characteristics of the studied population. The selection of these companies was based on specific criteria such as turnover, sector of activity, regional focus, and financial stability, which allows us to generalize the results to a broader population of businesses in the given industry. These criteria were established to ensure sufficient variability of the data and to provide deeper insights into the economic factors affecting the financial health of companies. The sample thus provides a representative view of the behavior and financial characteristics of similar companies, which helps achieve more accurate conclusions for the research in question.

3.1. Regression Analysis

In this study, regression analysis is used as part of analyzing the relationship between financial ratios and business modernization. Regression analysis is a statistical method used to study the relationship between one or more independent variables and one dependent variable. In this case, financial ratios, such as equity ratio, debt ratio, ROA, long-term debt ratio, operating income-to-equity ratio, long-term debt-to-equity ratio, and current ratio, were used as independent variables, and their relationship with business modernization (the dependent variable) was studied. Regression analysis is crucial for this study because it helps understand what factors may influence business modernization and provides quantitative information on how individual financial ratios correlate with the level of modernization. This analysis provides objective and measurable data that can support the conclusions and recommendations of this study. Performing regression analysis began with variable selection, focusing on independent and dependent variables. Independent variables, in this case financial ratios, were carefully chosen along with the dependent variable, i.e., the level of business modernization. Next, data were prepared, where the necessary data were obtained and recorded in a table in Excel. It is important to ensure that the values of independent and dependent variables are correctly recorded. The actual regression analysis was performed on a new Excel worksheet labeled “Regression Analysis”. Independent variables were listed in one column, with the dependent variable in another column. Cells containing regression results were then selected, and regression coefficients were calculated using the LINEST function. After obtaining the regression results, they were interpreted, including via analyzing regression coefficients, the R-squared value, standard errors of coefficients, and other statistical information. Visualization of the results through graphs is also an important step, allowing for a better understanding of the relationship between variables and an interpretation of regression results.

3.2. Cluster Analysis

In the presented study, cluster analysis is used to identify groups of companies based on their similar characteristics. Clustering allows us to group companies into different categories based on their financial indicators or other variables. This analysis was conducted to understand the diversity of modernization strategies among different groups of companies. Cluster analysis was performed on a dataset including companies of various sizes from various industries. Methods such as K-means clustering or hierarchical clustering were utilized. Groups of companies with similar characteristics in terms of modernization were identified. This analysis is important for exploration as it allows for a better understanding of the variability of modernization approaches in different types of companies. The results of cluster analysis are further interpreted and discussed in the context of strategic planning and effective management of modernization processes in the business environment. To conduct cluster analysis, variables were first identified for cluster analysis. These variables include various characteristics or attributes of the subjects under study. Then, data were prepared and recorded in a table in Excel. It is important to ensure that all necessary data are correctly recorded and have the same format. The analysis itself was conducted based on a clustering algorithm—the K-means algorithm. The results of cluster analysis were interpreted and visualized, allowing for a better understanding of the relationships between the subjects under study and identifying any patterns or groups. This interpretation is crucial for formulating conclusions and recommendations based on the results of cluster analysis.

4. Results

In the first part of the results, we will focus on the scoring of companies based on modernization. Areas studied were the presence of chatbots on websites, the number of likes and shares on Facebook, the number of days since the last post, and modern advertising, where a survey was conducted with 276 respondents.

For the modernization of companies today, technologies related to digitalization, automation, artificial intelligence (AI), the Internet of Things (IoT), and sustainability are particularly relevant. Different companies use various combinations of these technologies depending on their industry and needs. Key technologies include digitalization and automation of processes, such as ERP systems (Enterprise Resource Planning) for efficient resource management or CRM systems (Customer Relationship Management) for managing customer relationships. Automation of production processes and robotics lead to higher efficiency and lower operating costs. Artificial intelligence and machine learning enable predictive data analysis, process optimization, and automatic document processing, which significantly improves customer support through chatbots. The Internet of Things (IoT) is used to monitor and optimize production equipment using sensors and network technologies, especially in logistics and warehousing. Cloud infrastructure allows companies to use flexible and scalable IT solutions for data storage and processing, ensuring high availability and security. Cybersecurity is becoming a key component of business, as the protection of data and systems against cyber threats is essential, particularly with the deployment of firewalls, encryption, and network security. Sustainability and green technologies are another significant area of modernization, where companies are implementing energy-efficient systems and renewable energy sources, optimizing energy consumption, and reducing their carbon footprint.

For example, Nestlé uses production line automation and artificial intelligence for consumer behavior analysis and sustainable packaging. ČEZ and E.ON focus on renewable energy sources, smart grids, and predictive maintenance of equipment. Skanska invests in sustainable construction technologies, Building Information Modeling (BIM), and 3D printing of construction components. Telecommunications companies like Vodafone and T-mobile are implementing 5G networks and using the IoT for smart cities and smart homes, while Budějovický Budvar is modernizing its production and logistics processes through automation and digitalization of its distribution network. Avast specializes in cybersecurity and the development of software solutions for protection against digital threats, Jablotron uses IoT technologies for smart security systems, and Foxconn focuses on production automation, robotics, and the use of artificial intelligence in industrial production. These companies represent different industrial sectors, and each uses specific technologies according to its needs, increasing their competitiveness and innovation.

Figure 2 illustrates the total points of various companies, which indicate their degree of modernization. Each company is assigned a number of points indicating how significantly the company has modernized and to what extent it utilizes modern technologies and trends in business.

The data on the total number of points awarded to individual firms provide an overview of their overall success and significance in the context of the criteria under investigation. Companies such as Škoda with 881 points, Avast with 719 points, and Nestlé with 744 points have high scores, suggesting that they are considered leading entities in the areas under analysis. Conversely, companies such as Mondelez with 19 points, Energo-Pro with 101 points, and ČEPRO with 31 points have lower scores, which may indicate less importance or success in the monitored criteria.

Companies with a significant number of points, such as ČEZ with 229 points, O2 Czech Republic with 338 points, and Philip Morris ČR with 706 points, may be regarded as key players with high prestige and influence within the monitored context. On the other hand, companies with lower point totals, such as Mondelez with 19 points, Energo-Pro with 101 points, and Pilulka CZ with 204 points, may face challenges or have lesser influence in the given areas.

• Škoda Auto and AI

The following innovations showcase Škoda Auto’s commitment to leveraging AI for enhancing automotive experiences and optimizing processes:

• Sound Analyser:
  • Developed by ŠKODA AUTO and ŠKODA AUTO Digilab.
  • Utilizes AI for fast vehicle service needs identification through sound analysis.
  • Currently tested at 245 dealers in 14 countries.
  • Enhances service processes and customer care.
• Laura and ChatGPT:
  • Integrates ChatGPT into the voice assistant Laura.
  • Available in selected Škoda models.
  • Provides access to an extensive AI database even while driving.
  • Ensures data protection and offers various functionalities.
• Magic Eye:
  • Based on AI image recognition.
  • Identifies maintenance needs on the production line.
  • Installed at the main plant in Mladá Boleslav.
  • Enables timely response and process optimization.

4.1. Selected Current Ratio—Regression Analysis

The analysis aimed to explore the relationship between the current ratio (x) and the degree of modernization (y). A regression analysis (represented in

Table 2. Statistics and data types.

Column	Mean	Median	Minimum	Maximum	Std Deviation
Assets	43,223,620,000	5,884,484,000	1,634,000	1,029,000,000,000	155,950,100,000
Liabilities	44,152,740,000	6,208,682,000	1,634,000	1,029,000,000,000	1,576,270,000,000
Long-term liabilities	10,033,290,000	312,000,000	0.00	293,465,000,000	43,726,730,000
Current liabilities	20,183,420,000	2,431,950,000	558.00	537,095,000,000	81,590,860,000
Current assets	21,750,430,000	2,541,881,000	559.00	553,805,000,000	83,981,830,000
Operating profit (EBITDA)	4,839,491,000	312,319,000	−713,160,000	131,568,000,000	19,720,520,000
Equity	12,379,720,000	2,746,888,000	−735,780,000	198,440,000,000	32,727,610,000
Debt	30,785,250,000	4,289,650,000	561.00	847,119,000,000	127,053,800,000
Equity ratio	0.4438	0.4515	−0.5976	1.0	0.3333
Debt ratio	0.7322	0.5438	0.0123	599,546.00	1.0932
Current debt ratio	0.8841	0.7140	0.0059	3.1234	0.7330
ROA	0.0971	0.0652	−0.5792	1.0831	0.2089
Long-term debt to total assets ratio	0.1854	0.0624	0.0	1.0910	0.2738
Return on equity (ROE)	0.2927	0.1906	−0.2472	155,960.00	0.3456
Long-term debt-to-equity ratio	14.1475	0.1177	−7.7382	622.9879	92.8367

Source: Own source.

and

Table 3. Results of the regression analysis.

Correlation coefficient	0.504704
Reliability value R	0.254726
Set reliability value R	0.246061
Standard error of the mean	183.8871

Source: Own source.

) was conducted to better understand this relationship and determine how the current ratio may influence modernization. The correlation coefficient value, reaching 0.504704, suggests a moderately strong positive correlation between the current ratio and the degree of modernization. This value provides an indication of the extent to which these two variables are associated. The reliability value R was determined to be 0.254726, meaning that approximately 25.5% of the variability in the explained variable (degree of modernization) can be explained by the current ratio.

This provides a certain level of confidence in the model’s accuracy. The mean error, reaching a value of 183.8871, is a measure of the variability of data around the regression line. A higher value of this error may indicate uncertainty in the model’s parameter estimates. An analysis of variance (ANOVA) again yielded a statistically significant result (F = 29.3939, p < 0.05), allowing us to confirm that the regression model affects the explained variable (degree of modernization).

This means that the current ratio may influence modernization. The coefficient values of the regression model (155.5205 and 1.1028 for the current ratio) once again show how much the output variable (degree of modernization) will change in response to a change in the explanatory variable (current ratio). Residual analysis again showed various residual values, indicating random or systematic errors in the model. This suggests that the model could be improved or adjusted to achieve a higher accuracy and explanatory power. Overall, the results obtained suggest that the current ratio may influence the degree of modernization, but the model itself may not be sufficiently precise to fully explain the variability in the explained variable.

Figure 3 shows the probability distribution, and the graph with residuals is the subject of Figure 4.

4.2. Multilinear Regression Analysis

As part of this research, a multiple regression analysis was conducted. The results are as follows represented in following

Table 4. Regression statistics.

Correlation coefficient	0.605835317
R-squared value	0.367036431
Adjusted R-squared value	0.247286567
Standard error	183.1261212
Observations	45

Source: Own source.

,

Table 5. ANOVA.

	Difference	SS	MS	F	Significance F
Regression	7	719,503.2783	102,786	3.065025865	0.011970734
Residuals	37	1,240,801.522	33,535.2

Source: Own source.

and

Table 6. ANOVA 2.

	Coefficients	Standard Error	t Stat	p-Value
Intercept	324.7092563	120.6878361	2.69049	0.010643674
Equity ratio	−2.118755002	1.589762377	−1.33275	0.190763502
Debt ratio	0.727506102	0.710886088	1.02338	0.312773276
Current ratio	3.04227225	0.757297932	4.01727	0.000277113
ROA	1.055017292	0.97467409	1.08243	0.286065272
Long-term debt ratio	−1.063584102	1.009736442	−1.05333	0.299020892
ROE (PVH/VK)	−0.891916893	0.994330606	−0.897	0.3755173
DZ/VK	1.106085384	0.823711711	1.34281	0.187514838

Source: Own source.

.

4.3. Regression Statistics

Multiple R: Indicates the strength of the relationship between the dependent and independent variables, with a value of 0.6058 suggesting a moderately strong relationship.

R-squared: At 0.367, this means the model explains approximately 36.7% of the variability in the dependent variable, which is moderate but not very high.

Adjusted R-squared: With a value of 0.247, it suggests a lower level of explained variability, meaning not all variables in this model are sufficiently significant.

Standard error: 183.126, which is an estimate of the typical prediction error size.

ANOVA (analysis of variance):

Regression: The F-value (3.065) and its significance F (p-value 0.01197) indicate that the model is statistically significant at the 5% level, meaning the model as a whole has some predictive power.

Residuals and total: These values show the breakdown of variability between the model and random errors.

4.4. Coefficients of Individual Variables:

Intercept: 324.71—the model constant, which can be interpreted as the baseline value of the dependent variable when all other variables are zero.

Equity ratio: The coefficient −2.1188 suggests a negative relationship, but with a p-value of 0.1908, it is not statistically significant.

Debt ratio: Has a positive coefficient (0.7275), but with a p-value of 0.3128, indicating low significance as well.

Current ratio: With a p-value of 0.00028, it is statistically significant at a high level, and the coefficient of 3.042 suggests that this variable has a strong positive effect on the dependent variable.

ROA, long-term debt ratio, ROE, DZ/VK: None of these variables are statistically significant (all p-values are greater than 0.05), which means their influence on the dependent variable is not proven.

Residuals: This section shows the differences between observed values and values predicted by the model. Higher residuals indicate greater deviations from the model, suggesting that the model may not be accurate in some cases. The probability and percentile columns provide an idea of the distribution of residuals within the model.

4.5. Cluster Analysis

A cluster analysis was performed for businesses in the year 2022, the results of which are displayed in Figure 5. It is evident that the blue curve represents the first cluster, comprising stable businesses characterized by low equity and debt ratios. The third cluster (green curve, second cluster in our sequence) consists of moderately stable businesses, which concentrate significantly on equity ratio and debt ratio. The second cluster, third in the sequence, represents the cluster of unstable businesses facing significant operational challenges. These businesses focus more on the current ratio, identified as the indicator with the strongest relationship with modernization. Results of cluster analysis are displayed in

Table 7. Distance from cluster.

	Equity Ratio	Debt Ratio	Current Ratio	ROA	Long-Term Debt Ratio	ROE	DZ/VK	Cluster	Distance from Cluster
ČEZ	−0.49441	−0.69920	−0.42440	0.80737	0.308857	0.80737	−1.73845	1	0.73
Budejovic. Budvar	1.977652	1.398411	−0.42440	0.80737	0.308857	0.80737	0.562441	2	1.04
O2 CR	−0.49441	−0.69920	−0.42440	0.80737	0.308857	0.80737	−1.73845	1	0.73
Skoda	−0.49441	−0.69920	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.37
Vodafone	−0.49441	−0.69920	−0.42440	0.80737	0.308857	0.80737	−1.73845	1	0.73
Energet. a prum.ho	−0.49441	−0.69920	−0.42440	−1.21106	−3.165789	0.80737	−1.73845	2	1.39
Pilsner Urquell	−0.49441	−0.69920	2.303895	0.80737	0.308857	0.80737	0.562441	3	0.44
Tesco Stores	−0.49441	1.398411	−0.42440	0.80737	0.308857	0.80737	0.562441	2	0.90
Avast	−0.49441	−0.69920	2.303895	0.80737	0.308857	0.80737	0.562441	3	0.44
Skanska CZ	−0.49441	−0.69920	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.37
Nestle	−0.49441	−0.69920	2.303895	0.80737	0.308857	0.80737	−1.73845	3	0.55
Foxconn CR	−0.49441	−0.69920	−0.42440	−1.21106	0.308857	0.80737	0.562441	2	0.58
Hochtief	1.977652	−0.69920	−0.42440	−1.21106	0.308857	0.80737	0.562441	2	0.77
Seznam cz	−0.49441	1.398411	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.59
Rohlik cz	−0.49441	−0.69920	−0.42440	−1.21106	−3.165789	0.80737	0.562441	2	1.20
Kofola Group	−0.49441	−0.69920	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.37
Kiwi com	−0.49441	−0.69920	2.303895	0.80737	0.308857	0.80737	−1.73845	3	0.55
Zentiva	−0.49441	−0.69920	−0.42440	−1.21106	0.308857	0.80737	−1.73845	1	1.00
CETIN	−0.49441	−0.69920	2.303895	0.80737	−3.165789	0.80737	−1.73845	3	1.24
Czech Airlines T	−0.49441	1.398411	−0.42440	−1.21106	0.308857	0.80737	0.562441	2	0.61
Strabag	1.977652	−0.69920	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.87
T-mobile	−0.49441	1.398411	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.59
Sazka Group	−0.49441	−0.69920	2.303895	0.80737	0.308857	0.80737	0.562441	3	0.44
Philip Morris CR	−0.49441	1.398411	−0.42440	0.80737	0.308857	0.80737	0.562441	1	0.59

Source: Own source.

.

• Final Model—Proposed Model for Stability and Modernization Evaluation

As part of this work, a model for evaluating the stability and modernization of a business was developed and tested. The first part of the model focuses on assessing the modernization level of the business, requiring input data including the business’s latest Facebook post, its number of followers and likes on Facebook, the presence of chatbots on its website, and modern advertising techniques.

4.6. First Part of the Model—Modernization

The first part of the model is based on scoring various categories to assess the modernity of the business.

• Latest Facebook Post

For a business to score points for the latest post, we must go to its Facebook page and check the date of the last post. After entering this information into an Excel spreadsheet, the business receives a score based on the number of days since the last post. The number of days is entered into the spreadsheet, and points are awarded as follows: If the post was made within the last five days, 100 points are awarded. If the post was made within 15 days, 75 points are awarded. If the post was made within 20 days, 50 points are awarded. Finally, if the post was made over 30 days or more ago, zero points are awarded.

The first part of the table provides space for data entry. The second part includes the scoring categories, with the latest Facebook post listed first.

• Presence of Chatbots or Support on Websites

The second scoring criterion focuses on the presence of chatbots or support on the website. Here, by entering “Yes” or “No”, a business earns 100 points for having a chatbot and 0 points if it does not.

The Excel function for evaluating the presence of a chatbot is as follows: =IF(E2 = “Yes”, 100, 0).

Chatbots are becoming increasingly important tools for business modernization. They offer many advantages, leading to growth, prosperity, and better customer service. Companies that do not implement chatbots risk falling behind the competition.

• Points for Facebook Likes and Followers

Scoring considering Facebook likes and followers is another part of the model. A business’s Facebook page is assessed for the number of followers and likes, and these numbers are entered into the Excel spreadsheet to receive a score. Followers and likes are two separate categories, but the scoring method is the same. If a business has fewer than 1000 followers, it earns zero points. With 1000 to 4999 followers, it earns 10 points; 5000 to 9999 followers, 20 points; 10,000 to 49,999 followers, 30 points; up to 100,000 followers, 40 points; and up to 500,000 followers, 100 points. Over 1,000,000 likes or shares earns 300 points.

The Excel function for scoring followers and likes is as follows:

=IF(B2 < 1000, 0, IF(B2 < 5000, 10, IF(B2 < 10,000, 20, IF(B2 < 50,000, 30, IF(B2 < 100,000, 40, IF(B2 < 500,000, 100, IF(B2 < 1,000,000, 200, 300)))))))

• Points for Modern Advertising

Modern advertising is a key component of business modernization. In this case, a survey was conducted on social networks, where respondents rated whether the advertising was modern and interesting. The business must similarly conduct a survey, asking if their advertisement is interesting. The percentage of “Yes” responses is then entered into the spreadsheet to receive a score.

• Total Scoring

After all the points are summed, the table will determine whether the business is modern, moderately modern, or not modern. The maximum score is 900.

• Less than 300 points: not modern.
• 300–600 points: moderately modern.
• More than 600 points: modern.

4.7. Second Part of the Model—Stability

The second part of the model focuses on the stability of businesses, assessed based on ratio indicators. The selected indicators are the equity ratio, debt ratio, current ratio, ROA, long-term debt ratio, ROE, and the ratio of long-term liabilities to equity. First, each indicator must be calculated, and then, the data are entered into the table to calculate a score.

• Equity ratio:

If the equity ratio value is less than or equal to 0.5, the business receives 100 points; otherwise, it receives zero points.

The function for this value is =IF(C3 <= 0.5, 100, 0).

• Debt ratio:

If the debt ratio value is less than or equal to 1.3, the business receives zero points; otherwise, it receives 100 points.

The function for this value is =IF(D3 <= 1.3, 0, 100).

• Current ratio:

If the current ratio value is less than or equal to 1.3, the business receives zero points; otherwise, it receives 100 points.

The function for this value is =IF(D3 <= 1.3, 0, 100).

ROA

:

If the ROA value is less than or equal to 0.05, the business receives zero points; otherwise, it receives 100 points.

The function for this value is =IF(E3 <= 0.05, 0, 100).

• Long-term debt ratio:

If the long-term debt ratio value is less than or equal to 0.5, the business receives 100 points; otherwise, it receives zero points.

The function for this value is =IF(F3 <= 0.5, 100, 0).

• ROE:

If the ROE value is less than or equal to 0.1, the business receives zero points; otherwise, it receives 100 points.

The function for this value is =IF(G3 <= 0.1, 0, 100).

• Ratio of long-term liabilities to equity:

If the value of the ratio of long-term liabilities to equity is less than or equal to 1, the business receives 100 points; otherwise, it receives zero points.

The function for this value is =IF(H3<=1, 100, 0).

• Total Scoring

After all parts are scored, the total points are tallied. The maximum possible score is 700.

• 700–500 points: stable business.
• 499–300 points: moderately stable business.
• Less than 300 points: unstable business.

The function for this is =IF(R3 >= 500, “stable”, IF(R3 >= 300, “moderately stable”, “unstable”)).

4.8. Third Part of the Model—Cluster Analysis

The final part of the model involves obtaining results from the cluster analysis, performed using the Statistica tool from TIBCO. It requires importing data from a group of businesses, not just a single business. To obtain relevant results, business data must be imported into the program. Any available dataset can be used, or the prepared dataset from this work, which includes 45 businesses, can be utilized. The business’s stability and modernization evaluation results are added separately, and the analysis in the “Statistics” section is run.

5. Discussion

5.1. Results of Modernization Scoring

The results of the analysis indicate diversity in the success and strategies of Czech companies in the field of online marketing and modernization. Some companies have a high number of likes on Facebook, which may indicate a strong online presence and successful social media strategies. For example, companies like Škoda, Nestlé, and Philip Morris ČR fall into this category, with a high number of points, while companies like Energetický a průmyslový holding (EPH) have a lower number of points, signaling less activity on social media. Social networks have quickly gained prominence over the years, as confirmed by [47]. Their article discusses the advantages and limitations of social media as a strategic tool for organizational marketing management which has taken business management by storm and concludes by presenting significant recommendations for organizational managers.

The first research question examines which Czech companies focus the most on modernization and modern advertising. An analysis identified 45 high-turnover companies from 2021 and 2022 as a representative sample. This study assessed their online presence and advertising success through the number of likes and followers on Facebook and their ratings for modern advertising from a questionnaire survey. Companies like Škoda, Nestlé, and Philip Morris CR demonstrated a strong online presence, while others like Energetický a Průmyslový Holding (EPH) had lower social media activity.

The results show significant differences in advertising ratings, with Budějovický Budvar, Škoda, and Pilsner Urquell receiving high evaluations, while companies like Foxconn Czech Republic scored lower. These findings align with previous studies emphasizing the importance of modern advertising and a strong online presence for business success. Overall, companies prioritizing modern marketing strategies, such as Škoda, Avast, Nestlé, and About You, tend to perform better, as reflected in their high engagement metrics on Facebook.

Although social media is a recent phenomenon, it has proven to be equally or even more effective than traditional marketing. Several organizations are now facing challenges related to their online presence to communicate with customers near and far. Additionally, the use of social media by some organizations starts with simple marketing and creating awareness of their products and services. This may progress to public communication and interactions with customers and other stakeholders. Despite these enormous advantages, there are significant challenges.

There are differences among companies in the number of Facebook followers identified in this research. Companies with a high number of followers, such as Škoda or Avast, may have a strong brand, and customer interest in their content on social networks is high. Conversely, companies with fewer followers, like Energetický a průmyslový holding (EPH) or Synot TIP, may need to improve their targeting strategy on Facebook. Regarding the assessment of the modernity of a company’s online profile, authors differ in opinions on relevant metrics. Some focus on quantitative indicators, such as the number of Facebook followers and likes and the presence of chatbots on the website. Others emphasize qualitative aspects, such as the perception of modern advertising by customers and online reviews [48].

Our criteria for assessing a company’s modernity (number of Facebook followers and likes, chatbots, questionnaire survey) include aspects from both approaches. However, it is important to emphasize that there is no universal set of metrics. A suitable combination of indicators should be chosen considering the specifics of the industry and target audience.

In addition to the mentioned metrics, it is recommended to consider other factors such as the originality of content, relevance of advertising, and overall online image of the company. The level of interaction with the audience (e.g., comments on posts) and the functionality of chatbots are also important.

Another perspective on the success of companies’ advertising strategies is provided by the evaluation of the questionnaire survey regarding modern advertising. Differences in the results of individual companies emerge here, where some, like Budějovický Budvar or Škoda, received high ratings for attractive and successful advertisements. Conversely, companies like Foxconn Czech Republic, Hochtief, and Mondelez received lower ratings, indicating that their advertisements are not as effective or interesting to survey respondents.

In the context of similar research, it can be noted that the results of this analysis reflect similar trends and strategies found in other studies. A study [49] examines the relationship between equity and investments in technology in Chinese companies. They found that higher equity can positively affect investments in modernization technologies, but this relationship can be complex and dependent on other factors such as industry and company size. The authors of [50] examine how social innovations influence the economic growth orientation of small- and medium-sized enterprises (SMEs) in mountain regions, which may be relevant for interpreting the results related to social media concerning the modernization of companies.

The authors of [51] focus on the impact of online advertising on corporate credibility and consumer behavior, which may be useful for understanding the results regarding modern advertising in the present study. Another study researches what enables firms’ performance in the context of Industry 4.0 (I4.0) [52]. The authors investigate how I4.0 shapes the effects of firm investments over time and whether the effects of people and equipment depend on innovative management approaches. These studies provide a useful framework for understanding the relationships between different aspects of a business, such as equity, social media, advertising, and the overall degree of modernization of companies.

5.2. Financial Analysis of Škoda Auto

Based on the financial analysis of Škoda Auto, it can be concluded that its financial stability is influenced by several factors. The horizontal analysis shows that after an active increase in 2018 and 2019, there was a decline, with the exception of a significant increase in 2022/2023, which may indicate extensive investments or acquisitions. The decline in equity in 2018/2019 and 2019/2020, along with an extreme drop in 2022/2023, signals potential financial difficulties. Additionally, the increase in long-term liabilities in 2018/2019 and a slight decline in the following years, along with a significant increase in 2022/2023, may suggest new loans or financing. The company’s liquidity has been decreasing, particularly in terms of cash liquidity, indicating challenges in covering short-term liabilities. The rising creditor risk ratio and leverage ratio suggest increased dependence on external financing, while the return on assets and return on equity exhibit a fluctuating trend, with a significant decline in profitability in 2022/2023. The Z-score gradually decreased until 2020, when it reached its lowest point, but in 2023, there was an increase, which may indicate an improvement in the financial situation. Overall, the financial health of Škoda Auto faces challenges, such as declining equity and decreasing liquidity, which require further analysis and measures to restore stability and competitiveness. A crucial element of business management is systematically conducting financial analyses based on both financial statements and accounting data, as confirmed by the author of [53], who also performed financial analysis but specifically for Polish companies. The aim of their article was to present Polish companies’ approaches to the use of financial analysis in operational and strategic management, and it presents the results of a survey conducted among a group of 248 companies.

This research focused on analyses of financial statements and accounting data. The originality of the research lies in the fact that, in addition to examining the use of specific analytical areas, their usefulness for managers was also subsequently verified. Data from the following categories were analyzed: type of business activity, company size, and scope of business operations. The results of this research showed that decision-making based on financial analysis in operational and strategic management is more of an art than a science.

The analysis of Škoda Auto’s financial situation reveals several key trends and events that have a significant impact on the overall stability and performance of the company. This comprehensive analysis includes a detailed view of the development of assets, capital, liabilities, revenues, profitability, liquidity, indebtedness, and other financial indicators from 2018 to 2023.

Tools, analytical techniques, and other methods can be used on financial statements for corporate analysis. They are a diagnostic tool for assessing financial activities, investment activities, and operating activities, as well as an evaluation tool for managerial and other business decisions. Managers, shareholders, investors, and all other stakeholders can utilize financial statement analyses or financial reporting analyses to understand the company’s status, as highlighted in the research [54].

5.3. Regression Analysis 2021 and 2022

In this study, the relationship between financial ratios and business modernity was examined. Data from financial statements were used, and regression analysis was employed. A similar topic was addressed by the authors of [55], whose study investigated the ability of financial and non-financial performance to predict the timeframe for financial report disclosure. Financial performance was measured using three indicators: profitability, liquidity, and solvency. Meanwhile, the non-financial performance variable was measured using the Corporate Governance Index (GCG) and the auditor’s reputation. The proposed model was tested based on quantitative data collected from 156 manufacturing companies listed on the Indonesian Stock Exchange (IDX) in 2018 and 2020. Multiple regression analysis was conducted for data analysis and interpretation.

Regression analysis concerning data from financial statements was also conducted by [56]. The main aim of their study was to examine accounting ratios and detect false financial statements. The study had a descriptive nature. Clustered data from a total of 239 companies listed on the Nigerian Stock Exchange covering a period of 5 years (2017–2021) were utilized. The study utilized secondary data. Historical data were obtained from the annual financial reports of 10 purposely selected firms from the service sector. The acquired data were analyzed using descriptive statistics, Pearson’s correlation coefficients, and clustered logistic regression analysis. The study results indicated that profitability has a positive relationship with the detection of false financial statements.

Given the results of the regression analysis, it is evident that there is a varying relationship between the financial performance of businesses and their ability to utilize modern business methods such as social media, chatbots, and modern advertising. Analyses of individual factors indicate variable significance and strength levels for this relationship.

The equity ratio and debt ratio exhibit a rather weak correlation with business modernization. While there is a statistically significant relationship between the equity ratio and the degree of modernization, it is rather weak, and the model is not very reliable in predicting modernization based on this indicator. Conversely, the debt ratio does not show a statistically significant correlation with the degree of modernization, suggesting that this factor does not significantly influence the variability in modernization.

A statistically significant, moderate correlation is observed between the current ratio and business modernization. This suggests that the current ratio may have some influence on modernization, although with limited effects. The operating income-to-equity ratio shows some relationship with modernization, but this relationship is neither strong nor statistically significant. Similarly, the long-term debt-to-equity ratio did not demonstrate a statistically significant relationship with modernization.

Overall, it can be said that there is some relationship between the financial performance of businesses and their ability to utilize modern business methods, but this relationship is not straightforward and may be influenced by many other factors. Further research should focus on identifying these factors and quantifying them more precisely to better understand the relationship between financial performance and business modernization.

However, the findings above are not true for the relationship between operating income and business modernization. The relationship between variables and the quality of the regression model was examined in an analysis of operating income from 2021. Regression statistics revealed a significant relationship between the variables, indicating a strong correlation between the independent and dependent variables. The correlation coefficient reached 0.8868, confirming a high correlation. The reliability R, measured by the value of R-squared, reached 0.7865, meaning that 78.65% of the variability in the dependent variable is explained by the independent variable. An analysis of variance (ANOVA) supported the significance of the regression model with an F-statistic value of 324.102814 and an extremely low significance of F (0.011970734), indicating a statistically significant relationship between the independent and dependent variables.

The regression model coefficients provide a detailed view of the contribution of each independent variable to explaining the dependent variable. The intercept, which is the value of the dependent variable when the independent variables are zero, reached 740822957.3. The coefficient for variable X1 was 0.146458865, meaning that each unit increase in variable X1 is associated with an increase in the dependent variable of 0.146458865 units.

These findings confirm the significant relationship between operating income and business modernization, suggesting that increasing the operating income may contribute to strengthening innovation and modernization efforts in companies. This relationship is crucial not only for businesses themselves but also for policymakers and industry leaders focusing on promoting growth and technological advancement within the economy. For policymakers, these results are valuable for shaping strategies and policies aimed at improving the business environment. For example, it may be recommended to introduce incentives and programs to support investments in modernization projects in companies with high operating incomes, potentially leading to increased productivity and competitiveness on both the national and international levels.

For industries, these findings imply that effective management of operating incomes may be key to innovation and technological growth. Companies that successfully invest their operating income into modernization processes are more likely to adopt new technologies quickly, improve production processes, and increase efficiency. This can help them maintain competitiveness, particularly in industries where innovation and modernization are critical to success.

These findings highlight the importance of supporting business modernization in the studied country as part of broader economic development. If a country provides businesses with an environment that fosters the growth of operating income while also offering opportunities to invest these funds into modernization technologies, the country’s industrial base may improve overall. This could have a positive impact not only on GDP growth but also on employment and technological levels, which is especially important for developing countries.

The main implications of this study are that the management and utilization of operating income should be emphasized in business modernization. Future research could expand the analysis to include additional variables, such as government incentives, availability of technological innovations, or the influence of global economic conditions, to provide a more comprehensive understanding of the factors influencing business modernization.

The main goal of the present paper was to construct a model to evaluate the stability and modernization of businesses through in-depth analyses of the connection between their financial performance and their ability to utilize modern business methods. This goal was fully achieved, thus fulfilling both the main and secondary objectives of this work.

6. Conclusions

This paper provides a comprehensive summary and evaluation of an extensive study that examined the relationship between the financial performance of businesses, such as Škoda Auto, and their ability to utilize modern business methods. The study meticulously analyzed a wide range of factors, including ratio indicators, regression models, and the impact of modernization measures on the corporate environment.

One significant example of a studied enterprise was the automotive company Škoda Auto. Financial analysis of this enterprise revealed key indicators of its stability and performance. Particularly, the results of cluster analysis provided a deeper insight into its financial situation and performance, which was important for understanding its market position and ability to compete within the industry.

This study also involved creating a model for evaluating the modernity and stability of businesses. This model combined various factors, including financial indicators, the use of modern technologies, and corporate strategies with the aim of providing a comprehensive tool for assessing modernization needs and businesses’ ability to adapt to changing market conditions and technological innovations.

The findings of this study clearly indicate that business modernization is a key factor for success in today’s competitive environment. Businesses that successfully integrate modern technologies and strategies have a higher chance of achieving a competitive advantage and sustainable growth. Analysis of various aspects of modernization revealed that the use of artificial intelligence, social media, chatbots, and other modern tools significantly contributes to process optimization, improves customer experiences, and supports effective business management.

Furthermore, this research confirmed the existence of a relationship between financial performance and business modernization, although this connection is not straightforward and may be influenced by various factors. While some ratio indicators exhibit a weak correlation with modernization, others have a statistically significant impact. For example, the ratio of equity to total assets and the current ratio are associated with modernization, although their influence is limited. Conversely, the ratio of long-term liabilities to equity was found to be insignificant in the context of modernization.

The conclusions of the regression analysis suggest that operating performance may play a significant role in the process of business modernization. The relationship between operating performance and other variables is statistically significant, indicating the potential influence of this indicator on predicting modernization. However, the strength of this relationship may be influenced by other factors not included in the analysis, thus requiring further research.

Overall, it can be concluded that the financial performance and modernization of businesses are interconnected, but their relationship is complex and multidimensional. Further research should focus on identifying additional determinants of modernization and quantifying them more precisely to better understand the dynamics of this relationship and provide insights for effective strategies for future business development. This work makes an important contribution to understanding business modernization and paves the way for further studies on this issue. The primary limitation of this research is the small amount of data; using a larger dataset could yield more precise results.