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Modelling impact of hybrid methodology on performance of IT-enterprises

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Parent Category: 2026

Category: Content №4 2026

Created on 24 August 2026

Last Updated on 24 August 2026

Published on 30 November -0001

Written by M. Bosovska, A. Piliukov

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Authors:

M. Bosovska, orcid.org/0000-0002-6021-5228, State University of Trade and Economics, Kyiv, Ukraine, e-mail This email address is being protected from spambots. You need JavaScript enabled to view it.

A. Piliukov*, orcid.org/0009-0008-0802-4361, State University of Trade and Economics, Kyiv, Ukraine, e-mail This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2026, (4): 200 - 210

https://doi.org/10.33271/nvngu/2026-4/200

Abstract:

Purpose. To develop and empirically verify a model-based approach to assessing the impact of a hybrid project management methodology on the performance of software development enterprises, taking into account the level of managerial maturity and the moderating role of artificial intelligence tools.

Methodology. The study is based on an original survey of 93 Ukrainian IT enterprises (2026) and combines cluster analysis (the k‑means algorithm with the silhouette quality criterion), multiple regression modelling (OLS), the Analytic Hierarchy Process (AHP) with verification of the consistency of expert judgements (CR < 0.1), and three-level scenario modelling. The enterprise typology is constructed by level of managerial maturity through the aggregation of managerial, engineering and business indicators.

Findings. A typology comprising three enterprise types is empirically confirmed: start-up, established Agile practices, and mature enterprise level. The regression model (R² = 0.957) revealed a statistically significant effect of the presence of a project management office, CI/CD maturity and AI readiness on the composite project performance index (PPI). The transformation of the management system is substantiated through three scenarios: baseline, formalisation of the hybrid methodology, and integration of artificial intelligence tools. An adaptive PPI structure with differentiated subindex weights and assessment time horizons is proposed; the projected performance gain falls within the range of 10–40 %, peaking at highly mature enterprises.

Originality. A multi-level model for assessing the impact of a hybrid project management methodology has been developed and empirically verified which, unlike descriptive maturity models, combines an enterprise typology based on managerial maturity, an adaptive composite PPI indicator and a four-stage scheme for transforming management practices. The model operationalises the non-linearity of the transition between maturity levels while accounting for the moderating role of AI tools and the resource constraints of the enterprise.

Practical value. The proposed model is intended to serve as an analytical instrument for substantiating managerial decisions concerning the adoption of a hybrid methodology and the integration of artificial intelligence at software development enterprises. Its application delivers a predictably positive economic effect through the reduction of losses, revenue growth and an increase in the intangible value of assets, and provides a quantitative rationale for investment decisions by demonstrating positive NPV and IRR.

Keywords: hybrid methodology, project management, composite PPI indicator, artificial intelligence, IT enterprises

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