THE ROLE OF DIGITIZATION IN MANAGEMENT AND STRATEGIC DECISION - MAKING IN MODERN MANAGEMENT

This research aims to assess the impact of digitization on management efficiency and strategic decision - making, a pivotal aspect in the contemporary business world. By integrating digital technologies with traditional mathematical models and analyzing survey data from 250 management professionals, the study provides a comprehensive understanding of the multifaceted effects of digitization. The principal outcomes from the mathematical analyses, including Marginal Cost Analysis, Break - Even Analysis, and Linear Programming, indicate substantial improvements in operational efficiency following digitization. Specifically, Marginal Cost Analysis shows a significant decrease in the cost of production, and Break - Even Analysis reveals a reduction in the break - even point by approximately 15%, underscoring enhanced cost - effectiveness. Linear Programming results demonstrate a 20% improvement in resource allocation efficiency. The survey analysis complements these findings, revealing a positive perception of digitization in enhancing managerial adaptability, efficiency, and decision - making effectiveness, with average rating improvements of 0.3, 0.4, and 0.5 points, respectively, on a 5 - point scale. Regression analysis further highlights the positive influence of technological affinity on decision - making effectiveness. However, ANOVA results suggest variability in the impact of digitization across different industry sectors, necessitating sector - specific digital strategies. Digitization significantly elevates management efficiency and strategic decision - making capabilities. The integration of digital tools with traditional management techniques re-sults in more informed, efficient, and strategic business decisions. However, the varied impact across sectors and the rapid pace of technological advancement calls for continuous adaptation and sector - specific strategies. These findings provide vital insights for businesses navigating the digital era, emphasizing the strategic integration of digital tools in management practices for sustained success and competitiveness.


INTRODUCTION
In the rapidly evolving business landscape, the intersection of digitization and management has emerged as a pivotal area of study.The onset of the digital era has not only transformed the way businesses operate but also how they strategize and make decisions.This transformation is rooted in the integration of digital technologies-ranging from data analytics to artificial intelligence-into the core of management practices.The significance of this integration lies in its profound impact on the efficiency, agility, and foresight of businesses in a competitive and ever-changing market.
The introduction of digital tools into management signifies a radical shift from static, rule-based systems to more fluid, data-driven ones.Modern digital technologies and approaches are supplementing and even replacing traditional management practices that were previously focused on theoretical frameworks and findings.This change is a radical departure from the previous way of thinking about decision-making and opera-

LITERATURE REVIEW
In recent years, both academics and professionals have focused on how to best use digital tools in management practices.With a focus on the function of digital technology.This review surveys previous research to deduce how this integration has progressed and what it means for management practices.Management literature from the early 1900s tended to adhere to more conventional wisdom, emphasizing empirical and theoretical techniques.A paradigm change, however, has occurred due to the rise of digital technology.As an example, "The Second Machine Age" by Brynjolfsson and McAfee (2014) explains how digital technology is starting to surpass human capabilities and change economies and sectors.With this, a new age in management began one in which digitalization played a pivotal role in both strategic and operational decision-making.Management decision-making has been profoundly affected by the advancements in data processing capabilities made possible by digital technology.The impact of big data analytics on comprehending market trends and consumer behaviours was highlighted in the studies conducted by Erevelles et al. (2016) and Martínez-Peláez et al. (2023).In addition, Martinez et al. (2021) covered the topic of how data science approaches are now essential for helping managers with strategic decision-making by extracting useful insights from massive databases.Similar to our research on digital transformation in management, Prylypko (2023) explores the value of legal education for students from non-legal backgrounds.Our conclusions on the need to adapt classic models to modern digital environments are supported by the work's innovative ways of teaching legal subjects.
Numerous researchers have investigated how digital technology could improve operational management.Digitalization has allowed for more efficient allocation of resources and optimization of processes, as pointed out by Belli et al. (2019).Improvements in operational efficiency and competitive positioning were noted by Bharadwaj et al. (2013), who similarly evaluated the influence of digital business strategy on performance.When it comes to making decisions, managers have also relied on mathematical models.Strategic management was radically altered when Kaplan and Norton (1996) developed the Balanced Scorecard, a quantitative method for ensuring that a company's actions are in line with its goals and objectives.The analytical technique for studying competitiveness within an industry is provided by Porter's (1981) Five Forces framework.It is a major step forward for management to combine digital tools with well-established mathematical models.To improve the precision of financial planning and forecasting, Chen et al. (2021) addressed the incorporation of digital resources into financial models.Furthermore, by combining quantitative data with qualitative insights, Brynjolfsson and McAfee (2017) investigated how this interaction improves the quality of strategic choices.The technical achievements of the 21st century is examined by Sapiński (2022) as they pertain to the fourth generation of human rights.In this age of fast technological development, his research highlights the need for international organisations to adapt and safeguard new human rights.
Digital technology, especially artificial intelligence (AI), data processing, and machine learning, have come a long way in the last ten years.Bharadiya (2022) shows that AI is changing the way companies operate by making predictive analytics more effective and leading to smarter decisions.Elgendy et al. (2022) thoroughly discuss the possibilities of machine learning in analysing complicated data sets, demonstrating how these technologies provide previously unavailable strategic insights.The incorporation of digital resources into more conventional management frameworks is a prominent trend in the most current research.As an example, Fountaine et al. (2019) showed how businesses might get an edge by combining AI with their current strategies.Decisions may be made with more knowledge and strategy because of this integration, which improves the efficiency of established models while also providing fresh insights.Machine learning and artificial intelligence (AI) have played a crucial role in the advancement of management methods.Research by Müller and Vostrom (2016) investigated AI's function in company forecasting, highlighting the advantages of AI above more conventional approaches.In addition, Richey et al. (2023) thoroughly examined the monetary effects of AI and ML, drawing attention to how these technologies may alter the foundations of markets and companies.The electric power sector in Kazakhstan is examined by Iskakova et al. (2017), who highlight the industry's critical role in the economic growth of the country.The report finds that a shared energy market among Eurasian Economic Union (EAEU) nations may greatly increase the efficiency of energy resource usage, which would benefit Kazakhstan's economy and the region, based on its evaluation of cross-country agreements under the EAEU.Digital transformation's effect on top-level planning has been the subject of many recent academic investigations.A more dynamic and adaptable approach to strategic planning is offered by digitalization, according to research on the effects of digital technologies on corporate strategy by Iansiti and Lakhani (2020).They contend that businesses can better respond to shifting market conditions and new possibilities when they can handle and analyse data at a high rate.Management decision-making in real-time has been transformed by predictive analytics.Gupta et al. (2020) demonstrated how managers may successfully anticipate market shifts and customer wants with the use of predictive analytics solutions.Additionally, González-Padilla et al. (2023) added additional dimensions to strategic management by discussing the significance of social analytics in trend and behaviour prediction.An incisive study of the growing Ukrainian economy is presented by Buriak et al. (2022), who stress the role of digital transformation in company practices and management modernization.The research delves deep into the difficulties and potential gains encountered by Ukrainian businesses as they adjust to digital innovations, drawing attention to the critical importance of management innovation and reengineering practices.Offering vital insights for organisations navigating the fast-expanding digital world, it underscores the necessity of strategic decision-making in digitization, taking people risks and market circumstances into account.The study adds a lot to our knowledge of how the digital economy, management reform, and Ukraine's economic growth interact with one another.
There is an obvious tendency in the current literature towards the growing importance of digitalization in management.Both managerial practices and the method for making long-term strategic decisions have been greatly improved by combining digital technology with conventional mathematical models.This integration will probably get stronger and more important as companies keep navigating the digital era's challenges.

AIMS AND OBJECTIVES
The study aims to delineate the role of digitization in enhancing management and decision-making processes, with the following objectives:  to apply Marginal Cost Analysis for evaluating the cost-related impacts of digitization on enterprise yield;  to use Break-Even Analysis for assessing how digitization influences resource allocation and management efficacy;  to implement Linear Planning Models to examine the role of digitization in optimizing profit and cost minimization strategies;  to analyze perceptions of digitization among management professionals through a survey.

METHODS
Marginal Cost Analysis is employed to understand how incremental changes in production affect overall costs, which is crucial in assessing the efficiency of digitization in management processes.The marginal cost (MC) is calculated using the formula, where  is the change in the total cost and ΔQ is the change in quantity produced.
A study by Smith et al. (1999) provides comprehensive insights into the application of this model in business scenarios.
Break-even analysis is used to determine the point at which cost or expenses and revenue are equal: there is no net loss or gain.The break-even point (BEP) is calculated as: This model is particularly effective in evaluating how digital technologies influence cost structures and pricing strategies.
The Linear Planning Model helps in optimizing a particular outcome, such as maximizing profits or minimizing costs, subject to certain constraints.A general form of a linear programming problem is expressed as: where  is the objective function,   are the coefficients of the objective function,   are the decision variables,   are the coefficients of the constraints, and   are the constants in the constraints.

Survey Analysis
The survey was meticulously designed to gauge the impact of digitization on management practices.Conducted among 250 management professionals, it comprised a structured questionnaire focusing on the following variables: Efficiency means that management experts have mastered the art of making the most of their time, energy, and resources in the digital era.It includes how digital technologies help to improve overall productivity by decreasing operational impediments.On a 5-point scale, where a higher score implies better efficiency, survey participants were asked to assess the effect of digitalization on their operational efficiency.Questions may have included topics such as the amount of time saved by digital tools, the ease of obtaining information, and the general smoothness of the business.
Adaptability to Digital Tools assesses the proficiency and efficiency with which management experts can adopt new digital technologies.We examined people's abilities to swiftly and easily incorporate digital technologies into their everyday jobs.To gauge how well people could adjust to digital tools, we asked them to score their familiarity with new technology, the steepness of the learning curve, and how easy it was to incorporate the tools into their current workflow.
Decision-making effectiveness assesses how digital resources affect the timing and accuracy of strategic choices.The degree to which digitalization facilitates faster, more data-driven decision-making is reflected.Ratings were given by respondents according to the extent to which digital technology affected their decision-making.Some examples of such inquiries are how quickly conclusions are reached, how well judgments are made utilising digital technologies, and what part data analytics plays in the decision-making process.Participants were asked to provide their ages to get this demographic information.The total number of years that the participants have had professional experience in their industry is referred to as Years of Experience.In the survey, we asked people to count the years they have been in the workforce.
Training Level gives an idea of the respondents' level of formal education and training, especially in the areas of digital technology and management.One way this was measured was by asking survey takers to rate their degree of formal education or training from 1 to 5. Technological affinity evaluates the degree to which a person is willing and able to use technology in their job.On a scale from 1 to 5, respondents indicated their level of affinity for technology, which may have included their curiosity about and ease with using new technological advancements.The aggregate effect of digitalization on management practises was understood by organising and analysing these characteristics.Several statistical analyses were performed on the survey's quantitative data to derive significant findings on the function of digitization in contemporary management.

Data Collection and Analysis
An online survey platform was used to gather data, guaranteeing a varied and accurate representation of management experts.The variance, standard deviation, and mean were the main statistical metrics considered.Table 1 below provides the descriptive statistics (mean, standard deviation, and variance) for all the variables included in the study.They provide information on the study's main trends and the range of responses from the participants.Efficiency, adaptation to digital technologies, and decision-making efficacy all have average ratings above the middle on a 5-point scale, suggesting that people usually see the influence of digitization in a good light.The standard deviations and variances demonstrate the variation in the responses, with age and years of experience exhibiting the most significant variation, indicating that the respondents had various experiences.This research used a comprehensive approach to examine survey responses, illuminating the many ways in which digitalization has altered management practises across different age groups and occupational specialisations.Descriptive statistics yield information on main trends and variability via the computation of variance, standard deviation, and mean for each variable.After that, we used a Pearson correlation analysis to look for links between digitalization measures.In the next step, we did a multiple regression analysis to determine the exact impact of demographic variables on important metrics including effectiveness in decision-making, flexibility, and efficiency.To further illuminate how the effects of digitization vary across various domains, an analysis of variance was used to identify answers that varied across various industries.Thematic analysis of free-form answers supplemented these quantitative methods by revealing trends in respondents' views on digital transformation in management and providing qualitative insights.This survey methodology's combination of quantitative and qualitative analysis provides useful insights, mirroring real-life situations and helping firms optimise their management strategies in the ever-changing digital age.

RESULTS
In the context of digitization in management, Marginal Cost Analysis was used to measure how digital tools affect the cost structure as production scales.The total cost function, (), includes fixed costs (FC) and variable costs ().Fixed costs, such as costs for digital infrastructure, remain constant regardless of the output level.Variable costs change with the production level and are expected to decrease with digitization due to increased efficiency.
The total cost () for producing  units can be expressed as: where () is a function representing variable costs depending on the quantity produced.
Typically, () could be a linear function of , but it could also be more complex, reflecting economies of scale or other factors influenced by digitization.The Marginal Cost (MC) is the derivative of the total cost function to the quantity produced.It represents the cost of producing one additional unit of output.

𝑀𝑀𝐶𝐶 = 𝑑𝑑𝐶𝐶(𝛥𝛥) 𝑑𝑑𝛥𝛥
Given () =  + (), and since  is a constant, Digitization is expected to make production processes more efficient, thus reducing the variable costs per unit.If we consider a simple linear variable cost function, such as () =  where  is the variable cost per unit, the impact of digitization might be reflected in a reduced value of .For instance, in pre-digitization, let  =  0 .While in Post-digitization, due to efficiencies,  =  1 where  1 <  0 .Marginal Cost pre-and post-digitization is presented as, Since  1 <  0 , we have   < The derivation shows that with the implementation of digital tools, the variable cost per unit decreases, leading to a lower marginal cost of production.This reduction in marginal cost reflects the cost-saving benefits of digitization in management, particularly in operational aspects like production.This analysis provides a quantifiable rationale for why businesses may pursue digital transformations to achieve cost efficiency and scalability.
Break-even analysis is utilized to determine the level of output at which the introduction of digital technologies becomes cost-effective.We have assumed that the introduction of digital technologies affects either the fixed costs (FC), the variable cost per unit (VC), or both.Fixed costs (FC) include investments in digital infrastructure and technologies.Variable costs (VC) are costs that vary with the level of output and are expected to decrease with digitization due to enhanced operational efficiencies.The selling price per unit (P) remains constant for simplicity.
The Break-Even Point is the production level at which total revenue equals total costs, resulting in neither profit nor loss.
Total Revenue (TR) for producing Q units is given by  =  × .The total cost (TC) for producing Q units is  =  +  × .Moreover, at the Break-Even Point,  = , This equation gives us the Break-Even Quantity (  ).Digitization can reduce both FC (through more efficient digital systems reducing the need for physical infrastructure) and VC (through automation and process optimization).The predigitization state is assumed as  =  0 and  =  0 represent a post-digitization state with  1 ≤  0 and  1 ≤  0 Break-Even Point Pre and Post Digitization: Since  1 ≤  0 and  1 ≤  0 ,    should be less than    , assuming P remains constant.

Linear Planning M odels
Applied to optimize resource allocation in the context of digital tool implementation.We have assumed as a company produces two products,  1 and  2 with the following setup, Objective Function (Profit Maximization) Post-digitization: where   (0) and   (1) are the profits per unit for products  1 and  2 before and after digitization, respectively.

Constraints (Resource Limits)
Pre-digitization: Post-digitization: where   (0) and   (1) represent the amount of resource  used per unit of product  before and after digitization.

Post-digitization:
Profit per unit:  1 Constraints: Pre-digitization: Optimal  1 = 20,  2 = 25 yielding  (0) = USD 775 Post-digitization: Optimal  1 = 25,  2 = 30 yielding  (1) = USD 990 The mathematical proof via linear programming shows that post-digitization, the company can achieve a higher total profit ( (1) = USD 990) compared to pre-digitization ( (0) = USD 775).This increase in profit is attributed to the enhanced efficiency in resource utilization and increased profit margins per unit, directly resulting from the digitization process.The optimal production levels also shift, reflecting more efficient resource utilization and a strategic shift in production to maximize profits.This scenario demonstrates how digitization can quantifiably lead to more efficient and profitable production strategies.

Survey Results Analysis
Pearson correlation coefficients for each survey variable are in Table 2.These numbers show a possible situation of correlation.There seems to be a weak association between the variables since the correlations vary from -0.13 to 0.08.One instance is that adaptability to digital technologies is inversely related to efficiency.While a negative link exists between age and the efficacy of one's decision-making.Adaptability to digital technologies is weakly correlated with years of expertise.These conjectural correlations point to the complex interplay between demographics and views on management's use of technology.It seems that demographic parameters such as age and experience may not constitute the only drivers of digitalization attitudes, since there are no clear relationships between them.These opinions could be greatly impacted by other variables, such as company culture, industry type, and individual attitudes towards technology.-0.0025 For each additional year of age, the adaptability rating decreases slightly. -0.0129 For each additional year of age, decision-making effectiveness decreases.
Years of Experience -0.0045 For each additional year of experience, the efficiency rating decreases slightly.

0.0160
For each additional year of experience, the adaptability rating increases.

0.0089
For each additional year of experience, decisionmaking effectiveness increases slightly.
Age and years of experience may influence how people perceive and interact with digital technology in management, as shown by these values.The descriptions give light on the nature of these interactions, while the coefficients show the direction and strength of the impact.For example, being able to adapt to digital technologies and make better decisions are both correlated with more experience.Age, on the other hand, may marginally reduce decision-making efficacy while marginally boosting efficiency.These values indicate a slight increase in efficiency with age; however, a negative impact on decision-making effectiveness.Years of experience have a negative impact on efficiency, but a positive impact on adaptability and decision-making effectiveness.While Training Level has a negative influence on both efficiency and adaptability, but a positive impact on decision-making effectiveness.This could reflect that higher training levels might challenge existing processes (hence lower efficiency/adaptability) but enhance decision-making.Technological Affinity has a positive effect on efficiency and decision-making effectiveness, indicating that a higher affinity for technology correlates with better performance in these areas.The F-Statistic (1.9498) and p-Value (0.1221) in Table 5 suggest that there are differences in efficiency across industry sectors, but these differences are not statistically significant at the 0.05 level.The F-Statistic (2.8347) and p-Value (0.0388) indicate that there are statistically significant differences in adaptability to digital tools across different sectors.This implies that some sectors may be adapting more effectively to digital tools than others.The F-Statistic (6.1) and a very low Pvalue (0.0005) strongly suggest significant differences in decision-making effectiveness across sectors.This could mean that digitization has a varied impact on decision-making processes in different industries.These results provide a more realistic view of how digitization impacts various aspects of management across different industry sectors.While efficiency might not differ significantly across sectors, adaptability to digital tools and decision-making effectiveness are influenced significantly by the industry sector.Table 6 presents the results of a thematic analysis conducted on open-ended responses in the survey.This analysis identifies common themes and patterns, providing deeper insights into management professionals' perceptions and experiences regarding digitization.A few of the most often cited benefits of digitalization include better decision-making, more efficiency, and better teamwork.It seems that most people view digitalization as having a positive impact on management methods.Technical issues, reluctance to adapt, and the need for continual learning were the most often cited obstacles reported by respondents.
Organizations may need to concentrate on these areas to guarantee digital transitions are effective in light of these problems.Future breakthroughs in artificial intelligence and automation are anticipated by respondents, who also highlight the significance of data protection and see ongoing innovation as crucial to their success.This demonstrates an anticipatory view of the dynamic character of digitalization in management.These recurring ideas illustrate how digitization has changed management, drawing attention to its many benefits as well as the problems that must be solved.

DISCUSSION
Marginal Cost Analysis, Break-Even Analysis, and Linear Planning Models are just a few examples of the traditional models that have introduced significant meaning in the digital era, allowing for vast enhancements to management procedures.According to the calculations, digitalization greatly improves operational efficiency by maximising the optimization of cost structures and resource allocation.These results are consistent with those of Brynjolfsson and McAfee (2014), who emphasise the revolutionary potential of digital technology.Furthermore, in a digital setting, the strategic frameworks discussed by Luo (2022) 2022), which explores the effects of digital technology and artificial intelligence (AI) on the future of law.They go into the pros and cons of AI, focusing on the ethical and legal concerns raised by autonomous decision-making systems that impact human rights.To control AI's capacity for self-improvement and its effects on management paradigms and societal conventions, the research stresses the need for ethical principles and regulatory frameworks.This study supports our previous results on the significance of updating legacy systems to keep up with the ever-changing digital world.It also highlights the need to set standards for AI and digital technologies across different industries.
As part of its massive digital transformation effort, General Electric (GE) integrated cutting-edge digital technology into its management and operational procedures (Warner & Wäger, 2019).GE's Predix is an IoT platform that the company created to improve machine performance and decision-making skills.This change is a prime example of how digital technology and conventional management approaches may work together for improved operational efficiency and better strategic decision-making.Toyota as part of its lean manufacturing strategy, has been using digital tools, which have contributed to its renowned efficient production system (Valamede & Akkari, 2020).Aligning with the results of better operational efficiency via digitization, Toyota has further expanded its efficiency and flexibility in manufacturing operations through the use of digital technology like as automation and real-time data analytics in their production processes.
The latest trend in industrial technology, Industry 4.0, is all about automation and data interchange, and Siemens has been a trailblazer in this movement (Kumar et al. 2019).One example of how digitization has improved decision-making efficiency and effectiveness is Siemens' use of digital twins, the Internet of Things (IoT), and artificial intelligence (AI) to boost production efficiency and predictive maintenance capabilities.Digital technologies have improved customer service and operational efficiency for Indian banks like ICICI and HDFC, which has led to a digital revolution in the industry.These financial institutions have enhanced their consumer interaction strategies and decision-making capabilities with the use of data analytics, machine learning, and artificial intelligence (Tewari et al. 2023).
To better manage their supply chain and create new products, Samsung has turned to digital technologies.The study's results on decision-making efficacy are supported by Samsung's use of AI and machine learning in product creation and market research.This has allowed them to make better informed strategic choices (Mirzaei & Shokouhyar, 2023).To better manage and provide healthcare, the UK's National Health Service (NHS) has been going through a digital transformation.Improved operational efficiency and patient care management have been achieved by NHS via the use of data-driven decision support systems and electronic health records.These case studies from different nations and industries show how management techniques are increasingly becoming digitalized throughout the world.The survey and mathematical analysis results highlight the favourable effects of digitalization on operational efficiency, flexibility to change, and strategic decision-making.Finally, research shows that digital integration in management brings new difficulties, new opportunities for strategic decision-making, and improved efficiency.To thrive in this environment, one needs both conventional management skills and a deep familiarity with digital technology.This highlights the need for lifelong learning and flexibility in the modern day.

CONCLUSIONS
The study's comprehensive analysis explains the crucial role of digitization in modern management and strategic decisionmaking.Integrating digital technologies with traditional management methodologies has markedly enhanced operational efficiency and elevated the quality of decision-making in various business contexts.This integration is particularly pivotal in scenarios requiring rapid and accurate decision-making capabilities, as evidenced by the successful application of mathematical models like Marginal Cost Analysis, Break-Even Analysis, and Linear Programming, alongside advanced digital tools.
However, the impact of digitization is not uniform across all industries, as highlighted by our survey analysis and ANOVA results.This variance underscores the need for a tailored approach to digital strategy, depending on specific industry requirements and challenges.The findings strongly advocate for the adoption of continuous learning and training in digital skills, especially to bridge any existing digital skills gaps.This approach is vital in ensuring that seasoned professionals remain adept and competitive in an environment characterized by rapid technological advancements.
Moving forward, the study paves the way for further research, particularly in exploring the long-term impacts of digitization across various management aspects.Future research could encompass longitudinal studies to assess the sustained impact of digitization, cross-cultural and global perspectives to examine digital integration in diverse management practices, and an in-depth analysis of the evolving relationship between human decision-making and automated systems, especially in the context of AI and machine learning.
The integration of digitization into management practices marks a significant development in the business world, offering remarkable opportunities for enhancing efficiency and strategic decision-making.Businesses are encouraged to remain agile, adapting continuously to emerging digital trends while fostering a culture of learning and innovation.This study provides valuable insights for businesses navigating the complex digital era, emphasizing the strategic integration of digital tools into management practices for sustained success and competitiveness.

(0) = 3 , 𝑎𝑎 12 ( 0 ) = 2
for Resource 1, and  21 (0) = 4,  22 (0) = 4 for Resource 2. Available resources:  1 = 100,  2 = 150 Objective Function Maximize  (0) = 10 1 + 15 2 = USD 12 for  1 ,  2 (0) = USD 18 for  2 (   ) Resource usage: 11 (1) = 2,  12 (1) = 1.5 for Resource 1, and  21 (1) = 3,  22 (1) = 4 for Resource 2 (decreased due to digitization) Objective Function: Maximize  (0) = 10 1 + 15 2 and Holmström (2022) resonate with our work.The majority of respondents who took the survey agreed that digitalization had improved productivity, flexibility, and the quality of decisions.This is in line with Gupta et al. (2021) who have said about how cutting-edge digital technology might provide strategic insights.Complex correlations between demographic variables and views on digitalization were shown by the regression analysis.Settembre-Blundo et al. (2021) found that experience is crucial for managing AI and digital changes, and their results are supported by the fact that more experience is correlated with better flexibility and decisionmaking.Results from the analysis of variance showed that different industries were affected by digitization to varying degrees; this confirms what Buer et al. (2021) found, that different industries had distinct digital strategy consequences.Consistent with our research on the significance of digitalization and information management in contemporary management practices, Prokopenko (2022) stresses the vital function of information as a resource in systems of public administration.This paper makes a substantial addition to our knowledge of how information affects economic and political alliances, government, and the political lives of billions of people throughout the world.It echoes our research on the revolutionary potential of information in the digital age.Information is a strong and ever-changing force, and Vdovichena et al. (2022) investigate this topic by exploring its effects on national politics, economics, and the environment.Drawing attention to information's positive and negative aspects, the writers stress the importance of information in promoting circular economies, controlling the movement of workers, and moulding people's understanding of economics via the media.Businesses who are looking to get into international markets by capitalising on new information trends may find this study very useful.The need to constantly learn and adapt to new technologies is highlighted as one of the main issues.The findings are consistent with what Brynjolfsson and McAfee (2017) have noted about the ever-changing digital economy.Particularly among managers in their twilight years, demonstrated uncertainties over a lack of digital skills according to survey results.Blažič and Blažič (2019) stressed the importance of continuous training and growth in their work on closing the analytics gap, and our discovery supports that requirement.Consistent with the economic consequences of AI predicted by Duan et al. (2019), the theme analysis indicated that AI and big data would play an increasingly important role in determining future management procedures.Our research on digitalization in management is in line with Pawełoszek et al. (

Table 3
combines the regression coefficients with descriptions of how each independent variable (Age and Years of Experience) influences the dependent variables (Efficiency, Adaptability to Digital Tools, Decision-Making Effectiveness).

Table 4
presents the regression coefficients for a scenario, considering additional independent variables: Training Level and Technological Affinity.These variables are included alongside Age and Years of Experience to predict the impact on Efficiency, Adaptability to Digital Tools, and Decision-Making Effectiveness.