Vol. 58, No. 4 (2025)

Background and purpose: This study aimed to fill a gap in the literature by identifying how employee burnout shapes decision-making styles in the post-COVID-19 business environment. The main goal was to examine the impact of three dimensions of burnout—exhaustion, cynicism, and professional efficacy—on four conflict-related decision-making styles: vigilance, buck-passing, procrastination, and hypervigilance.
Design/Methodology/Approach: A total of 567 employees from various companies in Croatia participated in the online survey conducted in March 2023. Multiple regression analysis examined the impact of exhaustion, cynicism, and professional efficacy on decision-making styles under conflict.
Results: The results of the multiple regression analysis revealed that professional efficacy leads to a vigilant decision-making style, while simultaneously diminishing procrastination, buck-passing, and hypervigilance. Cynicism, in contrast, was a positive predictor of procrastination, buck-passing, and hypervigilant decision-making. Finally, exhaustion was found to have a positive impact on hypervigilance.
Conclusion: The study is significant because it contributes to the body of knowledge on the impact of burnout dimensions on professional decision-making styles in organisational settings, and it also offers practical implications of considerable importance.

Keywords: Employee burnout, Decision-making style, Work-related stress, Croatian companies, Post-COVID-19 period

Cecília OLEXOVÁ

Bratislava University of Economics and Business, Faculty of Business Economics with seat in Košice, Košice, Slovak Republic

Lívia KNECHTOVÁ

Ernst and Young in Bratislava, Slovak Republic

Abstract

Background/Purpose: This paper investigates employer attractiveness from the perspective of different employee cohorts in Slovakia. The study aims to identify which employer attributes are perceived as most important and to investigate how these perceptions vary across different generational and educational segments. The research addresses current labour market challenges, including shortages of both highly and moderately skilled labour, further exacerbated by demographic ageing.
Methods: A quantitative research design was applied, using a questionnaire survey with a final sample of 481 respondents. A two-step cluster analysis using SPSS software was employed to group respondents with similar preferences.
Results: The results reveal that younger cohorts prioritise opportunities for development and reputation, while older generations emphasise salary and job security. Differences in perception were also observed across education levels. The findings highlight the need for a segmented approach in employer branding strategies, tailored to demographic and regional labour market specifics.
Conclusions: The study contributes to the literature by contextualising employer branding within the Slovak labour market and providing practical insights for organisations seeking to attract and retain diverse talent. These findings are relevant for both academics and HR practitioners aiming to develop more effective employer value propositions.

Keywords: Employer attractiveness, Employer branding, Attributes, Clusters, Intergeneration differences

Robert LESKOVAR

University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia

DOI: https://doi.org/10.2478/orga-2025-0021

Abstract

Background and Purpose: E-commerce has reshaped consumer behaviour by offering unparalleled convenience, variety and accessibility, while creating new opportunities for businesses to grow revenues. Despite its prominence, there remains a need for parsimonious models that explain online purchase intention in terms of core consumer perceptions. This study aims to develop and test a structural equation model (SEM) in which consumer perceived benefits and attitudes toward the web drive intention to purchase online.
Methods: An online survey was administered to 190 Slovenian consumers. Questionnaire items were drawn from established scales measuring (a) perceived benefits of online shopping, (b) attitude toward the web, and (c) online purchase intention. Internal consistency was assessed via Cronbach’s alpha. SEM was then applied using IBM SPSS AMOS to evaluate both measurement and structural components of the model, testing hypotheses that perceived benefits influence both attitude and intention, and that attitude further mediates intention.
Results: The survey instrument demonstrated excellent reliability (Cronbach’s α = 0.92). The three-construct SEM explained 74 % of the variance in online purchase intention. Fit indices indicated very good model performance (NFI = 0.969, NNFI = 0.970, CFI = 0.979, IFI = 0.979). All hypothesized paths were significant, confirming that higher perceived benefits enhance both positive web attitudes and purchase intentions, and that web attitudes further bolster intention.
Conclusion: This streamlined SEM offers a robust and well-fitting explanation of consumer online purchase intentions. E-commerce platforms can leverage these insights by emphasizing the specific benefits consumers value and cultivating positive web experiences to drive sales. The model offers both practical guidance for online retailers and a foundation for future research, such as incorporating environmental consciousness, to refine our understanding of sustainable e-commerce adoption.

Keywords: Attitude toward web, Perceived benefits, Online purchase intention, SEM, Consumer behaviour, e-commerce

Oleksandr DOROKHOV

University of Tartu, Department of Public Economics and Policy, Tartu, Estonia

Kadri UKRAINSKI

University of Tartu, Department of Public Economics and Policy, Tartu, Estonia

Deniss DJAKONS

ISMA University of Applied Sciences, Riga, Latvia

Olha KOVALYOVA

Sumy National Agrarian University, Department of Economic and Entrepreneurship, Sumy, Ukraine,

Liudmyla DOROKHOVA

University of Tartu, Department of Marketing, Tartu, Estonia

DOI: https://doi.org/10.2478/orga-2025-0022

Abstract

Purpose: This study is motivated by the importance of startups in economic growth and the need for methods to evaluate their success, considering risk and uncertainty. The objective is to analyze factors that influence startups, using factor and cluster analysis. The hypothesis that advanced business analytics in startup evaluation can enhance the quality of investment decision-making was tested.
Methods: The combination of quantitative and qualitative techniques was used. Statistics about 20 startups from Latvia, Lithuania, and Estonia over five years were processed to identify success drivers and to group startups by similarity. Machine learning and social media sentiment analysis were applied to assess non-financial indicators.
Results: The results showed that indicators such as projected profitability, social media activity, and innovativeness are significant for startup ranking. The share of traditional methods in the Baltic states was 55%, while modern tools were 45%, highlighting the role of digitalization in risk assessment. Startups with high clustering coefficients and positive mention sentiment demonstrated superior performance.
Conclusions: The study demonstrated that integrating business analytics and digitalization enhances startup evaluation. The model combines financial metrics with network and sentiment analysis, offering a comprehensive framework for investors. It confirms that data-driven methods improve decision-making, reducing investment risks.

Keywords: Startup evaluation, Business analytics, Digitalization, Baltic States, Economic potential, Social engagement

Istanbul University, Institute of Social Sciences, Transportation and Logistics Management Program, Central

Gültekin ALTUNTAŞ

Istanbul University, Faculty of Transportation and Logistics, Department of Logistics, Central (Beyazit) Campus,

DOI: https://doi.org/10.2478/orga-2025-0023

Abstract

Background/Purpose: This study investigates the mediating role of decision–making performance in the link between software performance and overall business performance in the logistics sector of an emerging economy. As logistics companies increasingly rely on digital infrastructures, understanding how advanced systems contribute to strategic outcomes is critical for sustaining competitiveness.
Methods: A conceptual framework was developed integrating ERP systems, big data analytics, and IoT applications. In this model, software performance is positioned as the independent variable, decision–making performance as the mediator, and business performance as the dependent variable. Data were collected from medium- and large–scale logistics firms and analyzed using regression and bootstrapping methods through SPSS and the PROCESS Macro.
Results: The findings reveal that software performance significantly improves decision–making performance (β = 0.552, p < 0.01), which in turn has a strong positive effect on business performance (β = 0.817, p < 0.01). The mediation analysis confirms that decision–making performance mediates the effect of software performance on business outcomes.
Conclusion: The results highlight the strategic importance of aligning digital capabilities with organizational decision processes. By demonstrating the mediating role of decision–making, the study highlights that the effective use of advanced analytical tools is crucial for optimizing performance and achieving a sustainable competitive advantage in logistics.

Keywords: Software performance, Decision–making performance, Business performance, TMS systems, Logistics industry, Emerging economy

University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia

DOI: https://doi.org/10.2478/orga-2025-0024

Abstract

Purpose: This study examines how chatbots, as part of generative artificial intelligence (GenAI), can assist human resource (HR) professionals in supporting more effective knowledge management (KM), especially knowledge sharing (KS). The research aims to understand the strategic roles of chatbots in Human Resource Management (HRM). It offers propositions for their effective deployment to support KS and enhance their utilisation within organisations.
Methodology: A systematic literature review (SLR) was carried out using the databases Web of Science (WoS) and Scopus. After applying inclusion and exclusion criteria, 16 relevant articles were selected for detailed analysis.
Results: The findings show that chatbots can significantly enhance KS by automating HRM processes. They enable personalised training, offer continuous support, and promote employee performance, engagement, and innovation. Furthermore, chatbots assist HR professionals in focusing on strategic tasks by lowering administrative workload. Several challenges are also identified, including ethical concerns, privacy issues, data quality problems, reduced social interaction, and risks to creativity and critical thinking.
Conclusion: Chatbots offer a transformative opportunity for HRM to enhance KS, organisational memory, and digital learning, thereby supporting competitive advantage in knowledge-intensive settings.

Keywords: Chatbots, Generative artificial intelligence (GenAI), Human resource management (HRM), Knowledge management (KM), Knowledge sharing (KS)