A HIERARCHICAL MODEL OF DUE DILIGENCE FOR MANAGING THE INVESTMENT ATTRACTIVENESS OF AN ENTERPRISE

Abstract

Introduction.  Enterprise investment attractiveness is one of the key objects of management under conditions of intensifying global competition for foreign direct investment (hereinafter – FDI) and the structural reorganisation of Ukraine’s economy driven by wartime challenges and post-war recovery priorities. In a global economy where capital is highly mobile and competes between jurisdictions in real time, the ability of national enterprises to demonstrate sustainable and verifiable investment attractiveness becomes a decisive macroeconomic factor that determines not only the prospects of the enterprise itself, but also the macroeconomic resilience of the country as a whole. In Ukraine’s post-war recovery, where the need to attract substantial volumes of foreign capital for modernising the production base, infrastructure, and technological renewal is estimated by international institutions at hundreds of billions of US dollars, the quality of pre-investment review procedures acquires not only corporate but also national-economic significance, since each failed investment case reduces aggregate foreign-investor confidence in the Ukrainian jurisdiction and raises the cost of capital attraction for all subsequent market participants. Over recent decades, Ukraine has demonstrated contradictory FDI dynamics: despite stable volumes of accumulated capital in certain sectors of the economy, an unjustifiably high frequency of failed transactions is observed, ending in bankruptcy of the investment target, prolonged legal disputes, or actual loss of investments. This duality has a specific source – information asymmetry at the pre-deal phase of the investment process, reinforced by the objectively lower quality and availability of public registries in emerging-market countries, as well as the limited application of international corporate governance and financial reporting standards by Ukrainian target enterprises. The root cause of such failures is not macroeconomic conjuncture, but the systemic deficit and qualitative defects of pre-investment review procedures (due diligence, hereinafter – DD): non-performance of DD, incomplete risk coverage within performed DD, or erroneous identification of specific risks become triggers for a significant share of loss-making investment decisions; moreover, the losses from such failures encompass not only the direct financial component (write-off of investments, legal costs), but also indirect components – reputational losses, opportunity losses from alternative transactions, and complications of subsequent transactions. Contemporary academic and practitioner literature emphasises the necessity of in-depth DD specifically in cross-border transactions involving emerging-market countries, since the frequency of unconsummated or failed transactions in this segment markedly exceeds the analogous indicator for transactions between developed economies; the critical determinant of success is not the volume of review performed, but its methodological structuring and depth. At the same time, the methodological approaches to structuring DD procedures available in academic literature and investment consulting practice decompose the review at only one level (by the list of DD types: financial, legal, commercial, operational, tax, HR/reputational, environmental) or, at best, two levels (DD type + main analytical directions within the type), providing conceptual orientation but allowing neither prior calibration of review depth in accordance with transaction value and complexity, nor retrospective identification of the analytical-decomposition level at which the DD team missed a critical risk. This limitation is especially problematic when an investment failure becomes the subject of judicial proceedings or internal corporate audit, as there is no formal grid of coordinates for localising the methodological failure and assigning responsibility among DD team participants (financial, legal, commercial advisers, independent auditors). The foregoing forms a scientific-practical problem that requires the development of a DD methodological toolkit with controllable depth of detail, retrospective diagnostic capacity, and universal coverage of the entire spectrum of DD types, suitable for practical application by investment banks, private equity funds, lenders, and consultants under emerging-market conditions.

Purpose. The purpose of the article is to demonstrate the author’s four-level hierarchical model of the algorithm for conducting due diligence procedures as a tool for managing enterprise investment attractiveness, which provides structured decomposition of the review by the criterion of depth and implements two fundamentally new managerial effects: prospective – at the DD planning stage, the investor calibrates resources, time, and cost of the procedure in accordance with the depth required for the specific transaction; retrospective – in the event of a risk materialising that was not detected during DD, the model allows precise identification of the level at which the omission occurred and the use of this conclusion to improve procedures in subsequent transactions. The tasks of the article are: substantiating the relevance of the problem through the construction of an integrated rating of sectors of the Ukrainian economy by actual investment attractiveness; the structural elaboration of the four-level model with its mechanisms of controllable depth and reverse transitions between levels; demonstrating the model’s diagnostic capacity on two judicially confirmed cases of failed investments in Ukraine’s processing industry, selected from a broader sample examined within the dissertation research; and the consolidated mapping of identified DD failures across all DD types to confirm the model’s universality.

Materials and Methods. The information base of the study comprised: official statistical data of the National Bank of Ukraine (statistics of FDI stocks by NACE at year-end) and the State Statistics Service of Ukraine (structure of gross value added production by NACE) for four years of observations, 2020–2023; materials of the Unified State Register of Court Decisions of Ukraine – resolutions and rulings of commercial courts of first, appellate, and cassation instances in the two analysed cases, in particular the ruling of the Commercial Court of Dnipropetrovsk Oblast of 20 April 2021 approving the register of creditor claims in case No. 904/6691/20, the resolution of the same court of 6 July 2021 declaring bankruptcy, the resolution of the Central Appellate Commercial Court of 22 November 2021, the resolution of the Supreme Court of 8 June 2022, and the ruling of the Shevchenkivskyi District Court of Kyiv of 30 November 2021 in the AMKR case; open corporate sources of the parties to the analysed transactions (financial statements, audit reports, securities prospectuses, corporate annual reports); works of foreign and domestic researchers of 2020–2025 on DD methodology, corporate governance, and the assessment of enterprise investment attractiveness; and the OECD Guidelines for Responsible Business Conduct as a reference international regulatory document. The study employed a complex of mutually complementary methods: system-structural analysis (for constructing the hierarchy of model levels, defining the linkages between levels, developing the reverse-transition mechanism, and comparing with existing single- and two-level frameworks); statistical analysis and the method of expert evaluations (for constructing the integrated rating of sectors of the Ukrainian economy by actual investment attractiveness as the arithmetic mean of sector shares in FDI stocks and the GVA structure); the case study method (for empirical verification of the model on the materials of specific transactions); retrospective analysis with elements of forensic investigation (for establishing the model level at which the DD team should have detected the critical risk and for localising structural failures of the review procedure across levels 1–4 of the model); and comparative analysis (for the consolidated mapping of DD failures of both cases across all DD types to verify the model’s universality).and the method of expert evaluations (for constructing an integrated rating of sectors of the Ukrainian economy); the case study method (for empirical verification of the model); retrospective analysis with elements of forensic investigation (for establishing the level of the model at which the DD team should have detected the critical risk).

Results. A four-level hierarchical model of the DD algorithm with controllable depth of detail has been proposed, which decomposes the review procedure by levels: DD type (level 1) → DD direction (level 2) → object of inspection (level 3) → subject of inspection (level 4) – and provides for reverse transitions between levels to refine risk hypotheses; the model is end-to-end and applicable to all seven DD types of the classical taxonomy (financial, legal, commercial, operational, tax, HR/reputational, environmental). An integrated rating of sectors of the Ukrainian economy by actual investment attractiveness for 2020–2023 has been developed on the basis of averaging the percentage shares of the sector in FDI stocks and the GVA production structure, which allows filtering out both speculative attractiveness (high FDI concentration with low macroeconomic return) and false significance (high GDP share without confirmation by international capital); in the resulting rating, the processing industry occupies the first position (24.58%), outpacing wholesale and retail trade (17.19%) and the extractive industry (11.05%) by 7.39 and 13.53 percentage points, respectively. The model provides two fundamentally new managerial effects: prospective – controllable calibration of DD depth to the value and complexity of the transaction (levels 1–2 for low-value transactions, 1–3 for medium-value, full deployment across all four levels for high-value and strategic transactions); and retrospective – precise localisation of the level at which the DD team missed a critical risk. Demonstration of the model’s diagnostic capacity was carried out on the materials of two judicially confirmed cases of failed investments in the processing industry. In the case of PrJSC “AC Bogdan Motors” (bankruptcy proceedings No. 904/6691/20 of the Commercial Court of Dnipropetrovsk Oblast, finally confirmed by the resolution of the Supreme Court of 8 June 2022), the register of creditor claims amounted to approximately UAH 6.7 billion, of which UAH 1.78 billion fell on UniCredit Bank AG and UAH 1.64 billion on JSC “Ukreximbank” (aggregate bank losses of approximately UAH 3.4 billion); structurally, the DD failure is localised at levels 3–4 across financial, legal, and commercial DD, in particular along the directions of geographical revenue concentration on the Russian and CIS markets and the concealed affiliation of a significant portion of future creditors with the ultimate beneficiary of the borrower (Sarevin Investments LTD, CIF “BRIZ”, LLC “Financial Company ‘Finvork’”), which allowed the related structures to gain control over the committee of creditors in the bankruptcy proceedings. In the case of PJSC “ArcelorMittal Kryvyi Rih” (privatisation of 2005 for USD 4.8 billion), cumulative investments over 17 years of operation amounted to more than USD 10 billion, of which over USD 6 billion went into production modernisation and environmental measures – equivalent to a doubling of the cumulative value of the investment project against the initial asset valuation; the DD failure is localised at levels 2–3 across operational, environmental, legal, and tax DD, primarily along the direction of underestimating the full cost of post-privatisation capital and environmental obligations of the new owner and the regulatory instability of Ukraine’s tax regime. The consolidated mapping of identified DD failures across both cases ensures coverage of 6 out of 7 DD types of the classical taxonomy (financial, legal, commercial, operational, environmental, tax), confirming the universality of the proposed model. The comparative characterisation of the model against existing frameworks demonstrates a qualitative gain in the criteria of depth of decomposition (4 vs 1–2 levels), controllability of depth to transaction value (full vs absent/partial), presence of reverse transitions between levels (provided vs absent), and the possibility of retrospective localisation of DD failure (to a specific subject of inspection vs impossible/to the direction level). The scientific significance of the results lies in filling the methodological gap regarding the hierarchical structuring of DD procedures by the criterion of depth and in shaping a holistic diagnostic toolkit that extends to all DD types simultaneously; the practical significance of the model is determined by the possibility of its direct application by investment banks and private equity funds – for calibrating DD depth to a specific transaction; by lenders – for identifying hidden structural vulnerabilities of the borrower’s business model; by recipients of investments – for self-assessment of investment attractiveness and the preventive addressing of typical DD failures; and by consultants and auditors – as a standardised framework for DD reports.

Prospects. Prospects for further developments include several interconnected directions. First, extending the model to the subsequent positions of the rating of sectors of the Ukrainian economy – wholesale and retail trade, the extractive industry, information and telecommunications – with adaptation of the subject-matter content of levels 3–4 to industry specifics. Second, developing quantitative metrics of DD depth (for example, the shares of model levels actually covered by the review) for correct matching with the value and complexity of the investment transaction and for formalising the “DD cost – DD depth – failure probability” relationship. Third, building software tooling (a DD-checklist generator) based on the model for the automated generation of industry-specific checklists for subject-matter inspection at level 4. Fourth, investigating the correlation between the completeness of DD across model levels and the observed profitability of foreign direct investment using an extended empirical sample of cases. Fifth, a separate promising direction is the expansion of the classical seven-type DD taxonomy: the classical list (financial, legal, commercial, operational, tax, HR/reputational, environmental) is already being supplemented by an eighth – technological DD (Technology/IT/Cyber DD), which encompasses software architecture, cybersecurity, data processing, and regulatory compliance (GDPR, NIS2); further evolution of the taxonomy is driven by the challenges of the time and is projected to lead to the formalisation of ESG-specialised DD (separated out from the HR/reputational and environmental blocks as an independent type under the influence of the CSRD/ESRS regulatory initiatives), AI/algorithmic DD (for verifying machine learning models and automated decision-making systems), climate-transition DD (for assessing the risks of transition to a low-carbon economy), and other new types corresponding to the specific regulatory and technological challenges of the period. The proposed four-level model is architecturally open to the integration of these new DD types as additional elements of the first level without any need to restructure the lower levels of decomposition, which ensures its long-term viability as a methodological toolkit under the dynamic evolution of pre-investment review standards. A separate promising vector is the adaptation of the model for Ukrainian creditor banks within formalised internal credit-DD procedures. Equally relevant remains an international comparative study of the model’s diagnostic capacity on cases of failed investments from other emerging-market jurisdictions. The concluding direction of further developments is the integration of the four-level model with quantitative methods for assessing investment attractiveness (discounted cash flow models and real options models) to obtain an integrated pre-investment valuation of the asset that combines qualitative verification of risks by DD levels with the quantitative calculation of the fair transaction price.investigating the correlation between the completeness of DD by the levels of the model and the observed profitability of foreign direct investment.