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Klaipėdos universitetas

Keywords: artificial intelligence

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ARTIFICIAL INTELLIGENCE, POWER AND DECISION-MAKING IN VENEZUELAN MANAGEMENT: A POSTMODERN READING

Jesus Cendros Guasch

https://doi.org/10.15181/tbb.v96i1.2799

Pub. online: 16 Jun 2026 Type: Article

Open Access

Journal: Tiltai Volume 96, Issue 1 (2026), pp. 28–40

Abstract

The emergence of artificial intelligence (AI) in contemporary management is transforming production processes, decision-making, and the symbolic frameworks through which managers interpret organisational reality. In Venezuela, this transformation is particularly complex, due to economic crises, institutional fragility, environmental volatility, and the need for rapid managerial decisions amid uncertainty. This article analyses the impact of AI from a postmodernist perspective, based on a survey of Venezuelan managers (N=150) from the industrial, commercial and service sectors, selected through convenience sampling due to infrastructural constraints. The findings show primarily the instrumental adoption of AI, oriented towards decision support and operational optimisation, but they also reveal persistent tensions between human intuition and algorithmic recommendation. Ambivalent perceptions regarding dashboards, Key Performance Indicators (KPIs), and AI’s capacity to faithfully represent organisational reality are identified. From a postmodernist perspective, these results suggest that AI not only automates tasks but also reconfigures the legitimacy of knowledge, managerial subjectivity and power relations within the organisation. The study concludes that the use of AI in Venezuelan management expresses a transition towards hybrid forms of decision-making, where human rationality, algorithmic calculation and adaptation to crisis conditions coexist.

Improving Sustainability in Logistics Through Artificial Intelligence and Distributed Ledger Technologies

Vadym Derkach

https://doi.org/10.15181/rfds.v47i3.2760

Pub. online: 4 Dec 2025 Type: Article

Open Access

Journal: Regional Formation and Development Studies Volume 47, Issue 3 (2025), pp. 37–44

Abstract

As global pressure increases for sustainable and transparent supply chains, logistics organisations are exploring ways to strengthen

environmental, social and governance (ESG) performance. This article examines how artificial intelligence (AI) and distributed

ledger technologies (DLT) contribute to ESG integration in logistics. The study applies a qualitative desk research approach based

on secondary data from 2017–2025, including sustainability reports, port authority publications, and the industry press. The comparative

case analysis covers four Baltic logistics actors: the Port of Klaipėda (Lithuania), Vlantana (Lithuania), the Freeport of Riga/

Baltic Container Terminal (Latvia), and HHLA TK Estonia (Estonia). The findings show that Klaipėda’s LNG, OPS, and hydrogen

projects enhance environmental outcomes; the Vlantana Norge case exposes social and governance compliance risks; and Riga and

Tallinn demonstrate governance-oriented digitalisation through 5G networks and blockchain documentation. AI primarily supports

efficiency and risk detection, while DLT secures the transparency and auditability of ESG data. Together, they function as complementary

enablers of ESG reporting, though broader adoption requires regulatory alignment, interoperability, and investment in the

digital infrastructure.

Innovative Analytical and Statistical Technology in Election Forensics

Yuliia Yatsyna Igor Kudinov

https://doi.org/10.15181/rfds.v40i2.2528

Pub. online: 10 Jul 2023 Type: Article

Open Access

Journal: Regional Formation and Development Studies Volume 40, Issue 2 (2023), pp. 28–42

Abstract

The article examines the crucial role of innovative analytical and statistical technology in electoral forensics, which are increasingly used for detecting and preventing electoral corruption and fraud. By analysing vast amounts of data and detecting anomalies, electoral forensic investigations can contribute to fair and transparent democratic processes. The research aims to explore the effectiveness of these technologies and their potential impact on improving the transparency and fairness of electoral processes, using a multi-method approach that includes analysing relevant documents, media coverage, public opinion, and recent fraud cases. The authors divide the implementation of innovative analytical and statistical technologies for combating election corruption into four groups. The first is the analysis of statistical data and research on corruption, including election processes, which can be called secondary data analysis. The second is the analysis of documentary data containing information on corrupt actions and offences, including election processes. The third is the development of mathematical methods and algorithms using cutting-edge technologies such as artificial intelligence and machine learning for detecting anomalies and hidden patterns. The fourth is experimental developments in information technologies as a means of ensuring proper governance and combating corruption. While the use of algorithms for detecting anomalies in electoral statistics data can be an important tool, it should be used with caution, and in combination with other sources of information, to avoid the consequences of delegitimising the election results.

Investigating Some Strategies for Construction of Initial Populations in Genetic Algorithms

Alfonsas Misevičius Dovilė Kuznecovaitė

https://doi.org/10.15181/csat.v5i1.1277

Pub. online: 8 Jan 2018 Type: Article

Open Access

Journal: Computational Science and Techniques Volume 5, Issue 1 (2017), pp. 560–573

Abstract

Population initialization is one of the important tasks in evolutionary and genetic algorithms (GAs). It can affect considerably the speed of convergence and the quality of the obtained results. In this paper, some heuristic strategies (procedures) for construction of the initial populations in genetic algorithms are investigated. The purpose is to try to see how the different population initialization strategies (procedures) can influence the quality of the final solutions of GAs. Several simple procedures were algorithmically implemented and tested on one of the hard combinatorial optimization problems, the quadratic assignment problem (QAP). The results of the computational experiments demonstrate the usefulness of the proposed strategies. In addition, these strategies are of quite general character and may be easily transferred to other population-based metaheuristics (like particle swarm or bee colony optimization methods).

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