Dalia Streimikiene

57195415199

Publications - 10

AN ANALYSIS ON LEADERSHIP AND DECISION MAKING ERRORS IN THE NEW ARTIFICIAL INTELLIGENCE INFLUENCED ORGANIZATIONAL ENVIRONMENT

Dalia Streimikiene Raluca Maria Iordache Delia Mihaila Victoria Rodica Cioca Ștefan Emil Ionescu

Publication Name: Polish Journal of Management Studies

Publication Date: 2025-06-24

Volume: 31

Issue: 2

Page Range: 123-137

Description:

The integration of Artificial Intelligence (AI) is rapidly changing how leadership and decision-making work in organizations today. AI's emergence has created a need to rethink traditional ways of leading. Leaders now face the challenge of incorporating AI tools into their decision-making, which affects corporate strategies and how well they operate. However, while this combination of leadership and AI calls for adaptive strategies, it also raises worries about possible decision-making mistakes. To handle these challenges well, organizations should develop a strong understanding of how leadership styles affect decisionmaking when faced with the pressures of AI. To understand this relationship, our study considered key aspects like organizational climate and emotional intelligence, which are vital for long-term change.

Open Access: Yes

DOI: 10.17512/pjms.2025.31.2.08

Decision support for sustainable circular food supply chain in Iran: A fuzzy multi-criteria approach

Tomas Baležentis Dalia Streimikiene Ahmad Bathaei Z. Ardakani L. Agnusdei

Publication Name: Computers and Industrial Engineering

Publication Date: 2025-11-01

Volume: 209

Issue: Unknown

Page Range: Unknown

Description:

As an interconnected network, the food supply chain links multiple actors across production, processing, distribution and consumption. While it plays a vital role in ensuring food security, safety and economic resilience, the sector also faces growing challenges related to its environmental impact and long-term sustainability. Addressing these issues requires a systemic shift toward sustainable circular supply chain models that support net-zero objectives, decarbonization pathways, and ecosystem-wide coordination. This study aims to explore the key factors influencing sustainable circular supply chain management (SCSCM) across five major sectors of the food industry in Iran: grain, dairy, meat, sugar and carbohydrate products. By incorporating the concept of dynamic capabilities into the supply chain context, this study underscores the importance of organizational adaptability and innovation in facilitating the transition toward circular, low-emission supply chains. A snowball-based literature review revealed a lack of prioritization frameworks tailored to the food industry in Iran. To address this gap, the fuzzy Delphi method (FDM) was used to identify critical factors, followed by the fuzzy analytic network process (FANP) to evaluate and rank them based on expert judgment. The findings indicate that supplier facilities, trade credit, supplier risk management, environmental policy and environmental costs are the five most critical enablers of circular and sustainable transformation within the food supply chain. These identified factors offer a foundation for policymakers and industry leaders to design long-term, ecosystem-oriented strategies that enable systemic change and accelerate progress toward net-zero goals within the sector.

Open Access: Yes

DOI: 10.1016/j.cie.2025.111403

Malta's low carbon transition towards sustainability

Yuriy Bilan Brian Azzopardi Rafał Kasperowicz Marcin Pinczynski Dalia Streimikiene

Publication Name: Sustainable Development

Publication Date: 2024-10-01

Volume: 32

Issue: 5

Page Range: 5120-5128

Description:

The transition to low-carbon energy and energy independence of a country play an important role in the sustainable development of its energy sector. Another important issue of sustainable energy development is the cost competitiveness in the generation process; with new renewable energy technologies, a sustainable energy transition to carbon-neutral society is possible. In this article, we present a view of sustainable energy transformation based on a case study of Malta. We have created a simulation of a Maltese electricity system with projected growth and dominance of photovoltaic energy in the electricity market. The study results suggest that a system with a high penetration of photovoltaics has significant advantage over a conventional system using fossil fuels. In particular, in the simulated Maltese system, the total annual cost of energy was reduced threefold, the CO2 emissions were reduced by 40%, and the energy independence of Malta increased by 60%. In the end, the article gives a recommendation for further research into the Maltese energy system.

Open Access: Yes

DOI: 10.1002/sd.2967

EXAMINING THE CRITICAL SUCCESS FACTORS FOR THE EFFICIENCY OF GREEN ENERGY PROJECTS

Aknur Zhidebekkyzy Dalia Streimikiene Assel Kozhakhmetova Aizhan K. Anarkhan

Publication Name: Polish Journal of Management Studies

Publication Date: 2024-06-24

Volume: 29

Issue: 2

Page Range: 328-345

Description:

In recent times, there has been a remarkable exponential interest in green energy projects, driven by their potential to significantly decrease greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. However, such high-tech projects' complexity, expensiveness, and high uncertainty level necessitate new ways of increasing efficiency. Therefore, this study, with its clear aim to evaluate the efficiency of green energy projects and identify the critical success factors that can enhance efficiency, remains a compelling and relevant research endeavour. The study employed a rigorous methodology, using multilinear regression analysis to survey 123 project managers from Kazakhstan. This allowed for a comprehensive comparison of the efficiency level of green energy projects and low-tech projects. Research results show that green energy projects exhibit the highest percentage of schedule overrun at 23.9% and cost overrun at 32.7%, indicating substantial delays in project completion and extra expenses in budget compared with low-tech projects. Moreover, green energy projects show the least favourable results (7.3) regarding technological performance. The study reveals the following critical success processes enhancing project efficiency: project planning, scope and cost management, communication, and team management. Based on the processes of the Project Management Body of Knowledge (PMBoK) Guide, an algorithm for managing green energy projects was developed. This tool equips project managers with a process-based map, enabling them to run their projects effectively and enhance their efficiency.

Open Access: Yes

DOI: 10.17512/pjms.2024.29.2.17

VARIANCE AND DEVIATIONS IN THE BUDGETS OF REGIONAL ENTERPRISES AS AN ELEMENT OF RISK MEASUREMENT IN THE PROBABILISTIC MODEL

Yuriy Bilan Łukasz Kuźmiński Tomasz Norek Dalia Streimikiene Marcin Rabe Zdzisław Kes Agnieszka Lopatka Katarzyna Widera

Publication Name: Romanian Journal of Economic Forecasting

Publication Date: 2024-01-01

Volume: 27

Issue: 3

Page Range: 120-139

Description:

The aim of this article is to develop models that can measure probabilistic budget volatility risk in a manner that is not dependent on the type of cost or financing unit. Budgets are essential tools in facilitating the management process of any organization, while budget control helps optimize resource allocation and enhance operational efficiency. Using the methodology of budget deviation analysis can significantly improve the management of organizational units. However, the authors identify a research gap in terms of both methodology and application when it comes to analyzing the risk of budget variances. To address this, the authors develop models based on the theory of extreme values. The models can determine the deviation level for a specific probability level and estimate the limit level of deviation for assumed probabilities. These models can be used to holistically evaluate the level of budget implementation in the enterprise, compare the quality of budget implementation overtime and across units, and identify materiality limits of budget variances. To validate the models, empirical data from the budget control system of a major European city university was used. Empirical distributions obtained from the data were used to determine budget variances that indicate the level of deviation for a given probability level.

Open Access: Yes

DOI: DOI not available

Impact of Information Communication Technology on labor productivity: A panel and cross-sectional analysis

Yuriy Bilan Rafał Kasperowicz Dalia Streimikiene Aviral Tiwari Yash Laddha

Publication Name: Technology in Society

Publication Date: 2022-02-01

Volume: 68

Issue: Unknown

Page Range: Unknown

Description:

This article examines the contribution of information and communications technologies (ICT) to labor productivity using panel data approach. The study covers the period of 2000–2015 for a complete dataset of 98 countries as well for three selected groups: low-income, middle-income, and high-income countries. The findings imply that telephone subscription and broadband subscription have a significant impact on overall labor productivity as well as labor productivity of service sector. The ICT affects the labor productivity, so investing in Information Communication Technology is necessary to increase the labor productivity.

Open Access: Yes

DOI: 10.1016/j.techsoc.2022.101878

MULTI-CRITERIA DECISION ANALYSIS OF CIRCULAR ECONOMY PERFORMANCE IN THE BALTIC STATES: A COMPARATIVE EVALUATION

Tomas Baležentis Dalia Streimikiene Justas Streimikis Ahmad Bathaei

Publication Name: Journal of Business Economics and Management

Publication Date: 2025-10-10

Volume: 26

Issue: 5

Page Range: 1050-1070

Description:

This study embarks on a comparative evaluation of Circular Economy (CE) performance in the Baltic States (Latvia, Lithuania, and Estonia) using a ro-bust multi-criteria decision-making (MCDM) framework. Drawing on 22 key indicators, the research applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to systematically rank the country-level CE implementation across five thematic dimensions: production and consumption, waste management, secondary raw materials, competitiveness and innovation, and global sustainability. The results reveal that Latvia ranks highest, followed by Lithuania and Estonia, underscoring significant differences in waste management efficiency, investment in CE sectors, and material self-sufficiency. The main contribution of this paper lies in the development of a comprehensive, quantitative bench-marking framework that integrates multiple CE indicators and MCDM methods to assess national performance in a data-driven manner. The methodology developed here can serve as a replicable model for CE assessment in other regional or national contexts.

Open Access: Yes

DOI: 10.3846/jbem.2025.24717

Innovative knowledge-based system for forecasting daily hotel operations amid external events using multi-source data: A time-varying parameter state-space model

Tomas Baležentis Dalia Streimikiene Sichao Chen Ji Chen Qinglin Yu Kang Tong

Publication Name: Journal of Innovation and Knowledge

Publication Date: 2026-01-01

Volume: 11

Issue: Unknown

Page Range: Unknown

Description:

Forecasting hotel occupancy during external shocks is particularly challenging due to their disruptive effects. This study develops a forecasting framework that integrates multisource data using a time-varying parameter state-space model (TVP-SSM). In this framework, search engine data (SED) are used to construct exogenous variables, intervention variables are used to reflect the severity of external shocks, and holiday and weekend dummy variables are used to capture the seasonal effect. The empirical study used a dataset from the hospitality industry in Hangzhou, China, covering the period from October 1, 2019, to October 28, 2021, and identified the COVID-19 pandemic as an external shock. The results show that TVP-SSM can effectively simulate the dynamic impact of external events and the periodical effect on hotel occupancy. Additionally, the prediction accuracy of TVP-SSM with intervention variables and periodical variables (TVP-SSM-1) exceeds that of competitive models. Specifically, compared to the naïve model and TVP-SSM without intervention variables and periodic variables (TVP-SSM-2), the prediction accuracy, measured by the root mean square error (RMSE) and mean absolute percentage error (MAPE), increased by 86 % and 87 %, respectively, and by 74 % and 76 %, respectively. These results indicate that the forecasting framework proposed in this study exhibits superior forecasting performance and demonstrates its capability for dynamic impact analysis of hotel occupancy at the industry level under external shocks.

Open Access: Yes

DOI: 10.1016/j.jik.2025.100858

Identifying Critical Areas in Industrial Employment: Emerging Hot Spot Analysis of Workforce Expectations

Adriana Grigorescu Camelia Speranta Pirciog Cristina Lincaru Dalia Streimikiene Narcis Copcă

Publication Name: Economic Computation and Economic Cybernetics Studies and Research

Publication Date: 2025-01-01

Volume: 59

Issue: 4

Page Range: 269-291

Description:

This paper investigates the spatiotemporal distribution of employment expectations in industry (BS-IEME-BAL) between 1992 and 2025, using DG ECFIN Business and Consumer Surveys (BCS) data. The main aim is to identify areas with critical dynamics of the industrial labour market and to assess regional trends in labour fluctuations. The method used is based on Emerging Hot Spot Analysis (EHSA) in GIS, which allows the detection of regions where employment growth or decline has a persistent, oscillating, or emerging trend. The algorithm classifies areas according to the intensity and persistence of variations, allowing for anticipation of the impact of economic transformations on the industrial labour market. The study highlights critical regions where public policies can intervene to mitigate the negative effects or to support sustainable employment growth. Identifying hot spots in the industry contributes to the development of adaptive territorial strategies, based on spatial data, for the efficient management of structural changes in the labour market.

Open Access: Yes

DOI: 10.24818/18423264/59.4.25.15

Cloud Model-Based Improved Evidence Theory and Its Applications to Power Systems

Tomas Baležentis Jinming Zhou Dalia Streimikiene Hongqing Zhang Qin Zhou

Publication Name: Romanian Journal of Information Science and Technology

Publication Date: 2026-01-01

Volume: 29

Issue: 1

Page Range: 41-52

Description:

In order to cope with the complex features of ambiguity, randomness and uncertainty in multi-attribute decision-making problems, this paper introduces the DempsterShafer evidence theory in the framework of cloud modeling. First, a cloud model is used to calculate the affiliation of each evaluation metric, which was subsequently converted to a basic confidence assignment function. Second, the game theory idea is borrowed to combine the dynamic and static weights of the evidence in the game, to improve the traditional evidence theory and realize the effective integration of information. The idea of average fit is identified again, and a comprehensive evaluation conclusion is drawn by comparing the closeness of the evaluation object to the optimal and worst solutions. The new electric power system investment project is illustrated, and the applicability of the algorithm is verified.

Open Access: Yes

DOI: 10.59277/ROMJIST.2026.1.04