Introduction
Decision-making methods in business, as in any other industry, are a multi-criteria task, often requiring preliminary and subsequent assessment of all possible options. The complexity of each case is determined by many factors that must be considered in the proposed solution. An incorrectly made decision can lead to various negative consequences that require a quick response and the adoption of new, more hasty actions. On the other hand, well-established control over the methods used in this field can lead to success and create a fertile ground for further action. Therefore, using suitable mechanisms and techniques to critically evaluate and find the proper methods for specific tasks critically is an urgent issue in the business industry and management.
Using Models in Decision‐Making
Given the current trend in many sciences towards modeling, management and business are not left on the sidelines. The literature offers a wide range of decision-making methods for modeling complex business processes. Modeling in decision-making is often applied in three application areas: supplier selection and rational distribution of orders, evaluation and selection of goods or objects, personnel selection and human resource management (Kazimieras Zavadskas et al., 2019). However, the modeling techniques differ from task to task.
There are so-called multi-criteria decision methods that use the theory of probability, fuzzy mathematics, and logic in their methodology. Since some aspects of various processes are of the nature of uncertainty, they contain inaccurate or incomplete data. In addition, the use of such methods helps to plan for contingencies as much as possible. As a rule, clear, fuzzy, or hybrid decision-making methods are often used for logistics and transportation problems. In addition, the same methods are widely used in selecting suppliers and assessing the quality of services and economic activity (Kazimieras Zavadskas et al., 2019). These tasks require effective information management with various alternatives and the search for the optimal choice of solutions.
At the same time, a method is not suitable for cases in which there is uncertainty and imprecision. Decision Tree Analysis can help with problems with a finite number of possible solutions. This approach is most widely used in interaction with a client, but sometimes it finds its application in solving problems of human resources and personnel management (Lipyanina et al., 2020, Liu, 2020). Tasks involving human interaction are most suitable for this analysis, which is quite simple to implement with the help of current computer technology.
The management of enterprises is constantly looking for new methods due to constantly growing external restrictions, such as tightening of legislation, competition, and much more. In such conditions, models that consider these limitations and use the full potential of the company’s resources are precious. Research in this area often uses the method of computerization of the well-known convolution principle, adapted to the number and qualifications of experts, the degree of homogeneity, and non-statistical uncertainty of expert assessments (Loginovskiy et al., 2018). Decision-making methods with high constraints and conditions of instability are adequate to the current economic situation, complicated by the global pandemic and the turbulent social situation in many countries.
Data‐Based Decision-Making
Models can describe the possible properties of the system; they have predictive properties; however, to assess and find solutions, deeper analytics are required, represented by other approaches. At different business model levels, processing various types of information is required, ranging from market indicators of the economy, uploading to specific situations of customer comments (Borissova, 2020). However, these methods often pursue the same goal – optimization and efficiency gain.
Data-based decision-making is most widely used in education, where the impact of teacher decisions on student achievement is examined (Prenger & Schildkamp, 2018). However, there is often much more business data, and appropriate analysis can be auspicious (Akter et al., 2019). For example, international retail chains constantly conduct analytics that includes sales figures for certain products and the cultural and social characteristics of each region. Often, to determine the validity of a product, they resort to ABC and XYZ analysis, which shows which product is not liquid and which one needs to increase the supply (Konikov, 2019). It should be noted that these approaches require more time and labor to make a decision; however, the more data is used during the application of such methods, the more suitable a decision can be made.
In addition, such types of analysis as SWOT analysis, Pareto diagram showing the level of influence of factors on specific problems, Ishikawa diagram, showing the relationship of causes and identifying the main problem and development vector, become more effective with a large amount of analyzed data. If the organization’s capabilities allow, then in such cases, they resort to machine learning, which can analyze a large amount of data in an adequate time (Wang et al., 2020). Machine learning is possible through the use of neural network technology, which is also quite expensive and requires qualified specialists. In this regard, IT companies, due to their competence and global giants, have been more successful in applying such technologies, in particular for SWOT analysis (Hajizadeh, 2019). Such approaches are the foundation of many managerial and not only decisions, which are guided to determine the vectors of further development. Constant technological growth only contributes to the integration of information methods in the business industry, since in our time, it is worthy financing of such projects that are the main factor of their viability.
“Balanced” Decision‐Making
However, having competent data analysts can play a vital role in the success of an enterprise. Research has shown positive dynamics in the relationship between effective decision-making and the ability to conduct data analytics in an organization (Ghasemaghaei et al., 2018). A company’s decision-making culture is almost always dependent on government influence through funding, taxation, and legislation. It is noteworthy that research shows that dependence on power can be reduced through rigorous big data analytics. However, not all organizations can afford such costs for technology and skilled professionals (Frisk & Bannister, 2017). In addition, the approach is quite outdated when people in leadership positions make the most critical decisions in the organization. Therefore, the HiPPO decision-making standard, which implies that the highest-paid person decides the organization, is being replaced by a new decision-making process carried out by people with the appropriate competence in this matter (Pranjić, 2018). Of course, the role of leadership positions retains their responsibility in the most critical financing issues, without which it is impossible to allocate resources within the organization properly. However, their role in decision-making is reduced.
Nevertheless, despite integrating computer technologies into decision-making, most of the crucial actions remain with the person. For example, research of the investment market to increase the financial component of the organization can be done automatically. However, in the end, it is the employee or manager who decides where to invest money. Nowadays, there is a trend towards automation, even in consulting matters. Moreover, among executives and consumers, there is a trend towards high trust in robotic assistants (Lewis, 2018). These errors are most thoroughly worked out in the medical field, where their cost can be too high (Hough, 2020). In business, as in medicine, the expert opinion of a person with experience is still highly valued since any major tasks are multi-criteria in nature. The question is not in the efficiency of the computing power of computer technology but in the formulation of the problem, which should consider all sorts of factors that influence the result.
As a result, the most effective approach is precisely balanced decision-making, when it is possible to collect a large amount of data, carry out what computational assessment is possible using computer technologies, and give it an expert interpretation by a specialist. Of course, not all companies can afford such an integrated approach, but this is precisely the goal to strive to make critical decisions for the company.
Ethics in Management Decision-Making
Ethical issues are increasingly being raised in many areas of human life and are more relevant than ever. Ethics in business is often associated with corporate ethics, which includes comfortable working conditions and appropriate communication between employees and managers. Increasingly, environmental issues are raised in industries that harm the environment (Cothern, 2019). Ignoring these issues can lead not only to significant consequences in the world but also have an impact on the reputation of the organization itself. Nowadays, any large organization must meet specific social and environmental responsibilities (Li et al., 2020). Such events are encouraged by the state and make the company more attractive in the eyes of potential customers, regardless of the type of activity of the organization.
In addition, there is an unspoken social responsibility on the part of companies. This responsibility is also in line with current trends in the world. It is based on concern for employees, fairness, and the prevention of inequality, racism, and other ailments of modern society. Justice issues far from always govern even legislation at the state level in specific situations, but it is in the power of company leaders to correct these disagreements (Juujärvi et al., 2020). The trade unions partially solve such problems that employees join, but the actions of leaders always guide decision-making methods. They must maintain a friendly atmosphere in the team through a fair distribution of benefits and opportunities for pension contributions to the appropriate funds. Research is being conducted on various psychological aspects of both employees and managers in ethical decisions (Nguyen & Crossan, 2021). Such approaches include examining characters, looking at problems from a moral and ethical perspective, and uncovering the uncertainties of the wrongdoing and mistakes of both parties involved.
However, solutions often lead to crises of an international nature. As a result, many techniques are questioned, but crises contribute to developing solutions to this issue. Increasingly, organizations are focusing on the employee’s strengths, allowing him to realize his ambitions in the framework of his practice (De Graaf, 2019). This approach is most widely developed, again in medicine, so it makes sense to use various behavioral theories, which, with certain restrictions, can be transferred to the business industry (Meyer-Zehnder et al., 2017). Ethical approaches to decision-making methods should focus not only on justice and moral issues but also on the professionalism of employees and the psychological characteristics of their character.
Conclusion
Decision-making methods are highly diverse and are used for various kinds of tasks, even in the business industry. The ideal application of such methods should include the complex development of each of them for a specific type of problem. Models have predictive power and the ability to demonstrate current problems in terms of causation and quantification visually. The methodology of probability theory and fuzzy mathematics allows making decisions with the possibility of preliminary preparation for unforeseen situations or situations with a high degree of uncertainty and inaccurate data.
Collecting data opens up many ways to analyze it. Advances in technology have made it possible to evaluate large amounts of data in a short time frame. Advances in the integration of such methods in education allow them to be used in the business industry. Analyzing a large amount of data should always include expert analytics because quantitative indicators obtained by calculation cannot visually represent the complete picture of the results. Critical decisions and interpretations should be left to a person who can consider many criteria and factors.
In addition, current trends dictate the requirements of social and environmental responsibility. Ethical issues include adhering to these requirements in decision-making methods at any level of the organization’s structure. Social problems are widely studied in the medical field, so finding solutions in theories of behavior and character can help in such problems. The difficulty of following all the requirements and the cost of qualified experts and software for processing and analyzing big data always leads to an increase in the productivity and efficiency of decisions made, which has been proven by many studies.
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