Digital Experience: How to Find a Tool for Evaluating Business Economic Risk
Corresponding email: lin.stu@yandex.ru
Published at : 07 Dec 2020
Volume : IJtech
Vol 11, No 6 (2020)
DOI : https://doi.org/10.14716/ijtech.v11i6.4466
Lyukevich, I., Agranov, A., Lvova, N., Guzikova, L., 2020. Digital Experience: How to Find a Tool for Evaluating Business Economic Risk. International Journal of Technology. Volume 11(6), pp. 1244-1254
| Igor Lyukevich | Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade, Pe-ter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, St. Petersburg, 19 |
| Anton Agranov | The Faculty of Part-Time Studies Emperor Alexander I St.Petersburg State Transport University, Moskov-sky pr., 9, 190031, Russia |
| Nadezhda Lvova | Department of Theory of Credit and Financial Management St.Petersburg State University, Saint-Petersburg, Universitetskaya nab., 7/9, 199034, Russia |
| Liudmila Guzikova | Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade, Pe-ter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, St. Petersburg, 19 |
Risk
evaluation includes not only quantitative or qualitative assessment but the
choice of action that depends on the risk event. The paper highlights key
research in the field of risk assessment from 1921 to the present day. The
suggested concept of business eco-nomic risk evaluation is substantiated using
the criterion of information accessibility. One group of assessment techniques
is based on statistical analysis, namely insolvency risk assessment models
combined with a group of probabilistic ones. The alternative group includes all
techniques that differ from the accumulated data analysis (STAR - Strategic
Technology Assessment Review, HAZOP
Hazard and Operability Study, and FMEA
Fail-ure Mode and Effects Analysis qualitative assessments). Scenario,
list, and analogies techniques (SWIFT
Structured What If Technique, HACCP
Hazard Analysis and Critical Control Points, RCA Root Cause Analysis, BOWTIE, WCS
Worst Case Scenario) are more accurately characterized as an evaluation
of conditions and consequences of eco-nomic risks. Based on the advantages and
disadvantages of risk assessment techniques, this study proposed a
classification of the tools for evaluating business economic risk and an
algorithm for choosing a tool appropriate for the situation based on the
available information. The possibility and directions of practical
implementation, as well as the ex-isting digital assessment tool products, are
shown.
Digital experience; Economic risk; Evaluation tool; Qualitative risk assessment; Quantitative risk assessment
Scientific research into the phenomenon of economic risks dates back to the early 20th century. Knight was the first who suggested delineating risk and uncertainty when characterizing probabilistic events in the economic field (Knight, 1921). The next significant work was the study on risk assessment by Neumann and Morgenstern (1944). Formation of adverse outcomes assessment and management science took place in 1950–1990s. Arrow (Nobel Laureate) substantiated the impossibility of describing all risk options and showed that the best way to prepare for the onset of the risk event was to assess the consequences of its occurrence and the costs of eliminating them (Arrow, 1951). He also proved the theorem (Arrow's paradox) on the lack of techniques for combining individual preferences for three or more alternatives, which would satisfy some completely fair conditions and always gave a logically non-contradictory outcome (Arrow, 1951). Snider formulated the 'risk management' theory in 1955–1956 (Snider, 1991), and Gallagher gave a description of the risk manager profession (Gallagher, 1956).
With the abolition of the Bretton Woods system of fixed exchange rates,
the area of assessing and hedging risks in financial markets evolved. The works
of Merton (1973), as well as Black and Scholes (1973), are seen as the key
ones. In 1973, the Geneva Association, which united research in the field of
risk economics and risk insurance, was established. In the 1990s, risk
management obtained the status of the strategic management paradigm. The
concept of the internal risk control necessity was finally formed (COSO, 1992).
In the 2010s, debates on the innovation risk assessment system started (Nikolova et al., 2017). In this case, risk
minimization directions depended on whether it was relat-ed to the high- or
low-tech sector (Rodionov et al., 2018).
However, risk management in modern finance is one of the fastest growing but
actively criticized areas. In particular, Taleb’s black swan theory vigorously
and flatly criticized the practice of financial and economic decisions based on
the assumption that risk prevails over uncertainty (Taleb,
2007). Besides, risk assumption is currently prevailing in business
management, which defined the perspective of this study.
In case of an unfavorable event, the decision is either to accept the
consequences drawing on reserves or to take preventive actions. Thus, business
is likely to incur costs either after or before the risk event. At the same
time, due to the apparent transformation of all areas of the economy,
'classical' techniques, tools, and criteria are losing their practical
significance, and their actualization is necessary with allowance for the
processes of digitalization of the economy (Malevskaia-Malevich
et al., 2018).
Available estimates suggest that the quality of products increase when a
business risk management system is implemented (Hidayatno
et al., 2015; Pariaman et al., 2017). It was shown that with a quality
increase, the demand curve, in terms of the quantity of the product/product
price, shifts to the right, parallel to itself (Demidenko
et al., 2017). Efficiency rates are starting to grow. Owners pay more
attention to cash flow and real profits than to accounting (Dvas et al., 2018).
Risk research theory and practice have developed a wide variety of
approaches, techniques, and models based on both the quantitative and
qualitative analysis of business economic risk factors (Gissel
et al., 2007). Apart from this, a comprehensive view of the tools and
techniques of risk assessment and features of their application have not yet
been proposed. Additionally, the problem of finding and choosing a tool that
draws a conclusion on the level of business economic risk has not been fully
resolved. Risk assessment should not involve economically impractical periods
of time and other costs, and conclusions should be properly substantiated.
We assumed that evaluating business economic risk suggests a special
methodical toolkit aimed at quantitative or qualitative assessment of this
risk, as well as at the choice of one of the alternative options for actions
involving the onset of the risk event. The research hypothesis implies that as
part of the existing approaches to assessing business economic risks, it is
possible to formulate a search algorithm to find the optimal tool to evaluate
economic risk, including digital skills use.
A comprehensive weighty analysis of approaches for
evaluating the business economic risk has been carried out. This made it
possible to divide the existing approaches into two categories, economic risk
evaluation and evaluation of consequences of economic risk, and create a classification
of the currently known methods and models for risk evaluation. The peculiarity
of this concept is that it includes assessment not only in the context of
accessible and relatively transparent information but also in the presence of
significant information asymmetry. This gives an expanded view of the business
economic risk, which is an original solution, compared to other works in the
related field.
Systematization of methods and models, in turn, explores the
digital experience in each area under consideration. Approaches to the
practical implementation of STAR, HAZOP, and FMEA, which are qualitative risk
assessment methodologies, are also proposed. Identifying the advantages and
disadvantages of methods and models generated the algorithm for choosing a tool
for evaluating the business eco-nomic risk.
We agreed that digital experience is currently in progress by
means of Industry 4.0 technology (Bataev, 2018;
Berawi, 2018), but, at the moment, the main
problems of implementation of digitalization are related to national
institutional and legal norms, namely to the uncertainty of responsibility (Babkin et al., 2018).
Most statistical models of economic risk assessment involve
obtaining reliable results precisely in the areas for which they were created.
Therefore, broad extrapolation under basic conditions (e.g., industry, business
scale, etc.) leads to an inaccurate result. Unreliability of reporting will
also seriously distort the result (e.g., profits overstatement/ understatement,
working capital share, etc.). Thus, it seems that logit and probit models may
be modified by describing them as a system of inequalities, setting conditions
that the sum of the indices should be above / under the normative value. By
solving such a system, it is possible to set requirements for financial
indicators, which will ensure the formation of broader financial indicators
that reflect various risks.
This
research work was supported by the Academic Excellence Project 5-100 proposed
by Peter the Great St. Petersburg Polytechnic University
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