Vol. 58 No. 3 (2025)

Background/Purpose: ChatGPT’s rapid diffusion has transformed large-language-model (LLM) technology from a specialist tool into a mainstream companion for study and work. However, empirical evidence on what drives user satisfaction outside medical settings remains scarce. Focusing on future business and management professionals in Croatia, this study examines how perceived ease of use and perceived reliability shape satisfaction with ChatGPT and whether those effects differ between Generation Z (18–25 years) and Generation Y (26–35 years). Methodology: An online survey administered in August 2024 yielded 357 valid responses. The measurement model met rigorous reliability and validity criteria (CFI = 0.96, SRMR = 0.04).
Results: Structural-equation modelling showed that, in the pooled sample, ease of use (β = 0.42) and reliability (β = 0.46) jointly explained 72 % of satisfaction. Multi-group analysis revealed a generational split: both predictors were significant for Gen Z. However, only reliability remained significant for Gen Y. Gaussian graphical models corroborated these findings, indicating a densely interconnected attitude network for younger users and a reliability-centred network for older users.
Conclusion: The study extends technology-acceptance research to the management domain, underscores the moderating role of generation and illustrates the value of combining SEM with network analytics. Insights inform designers and educators aiming to foster informed, responsible and gratifying engagement with generative AI.

Keywords: Artificial intelligence, Large language models (LLM), Marketing, User satisfaction, Croatia, ChatGPT

Lukasz ZAKONNIK

Department of Computer Science in Economics, University of Łódź: Łódź, Poland

Maryna VOVK
National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine

Vanja BEVANDA
Juraj Dobrila University of Pula, Faculty of Economics and Tourism “Dr. Mijo Mirković”, Pula, Croatia

Daria MALTSEVA
HSE University: Moscow, Russia

Stanislav MOISSEV
HSE University: Moscow, Russia

Borut WERBER
University of Maribor, Faculty of organizational sciences, Kranj, Slovenia

Anja ŽNIDARŠIČ
University of Maribor, Faculty of organizational sciences, Kranj, Slovenia

Abstract

Funding: This work was supported by the Slovenian Research Agency; Program No. P5-0018 – Decision Support Systems in Digital Business and the HSE University Basic Research Program.
Conflicts of interest: All authors declare that they have no conflicts of interest.
Ethics statement: All procedures performed in this study involving human participants were in accordance with the ethical standards of the University of Maribor, Faculty of Organizational Sciences research committee (decision no. 514/5/2021/1/902-DJ) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Background and purpose: Technology acceptance has been researched for decades. While some technologies are widely accepted, others are perceived as a threat, such as microchip implants. In this study, a two-step structural equation modeling approach was used to evaluate a new research model on microchip implant acceptance.
Methodology: A structural equation modeling model was developed to identify what influences the perceived acceptance of microchip implants. To determine differences in attitudes toward microchip implants, the study was conducted in five Eastern European countries.
Results: The results show that the influence of the factors does not differ significantly across the countries studied. Age, trust, and perceived usefulness affected the overall intention to use microchip implants, while ease of use was significant in only one country. Differences were found in perceptions of the right to privacy and conspiracy theories. The usefulness of microchip implants in pandemic was significant in all countries.
Conclusion: Small differences in attitudes towards microchip implants suggest that a general model of microchip implant acceptance could be constructed based on the data collected. In addition to these findings, our study noted the lack of legislation for microchip implants in the region and a lack of knowledge about this technology.

Keywords: Microchip implant, Near field communication, Behavioural intentions, Structural equation model, Technology acceptance model

Katalin NISTOR
Semmelweis University, Faculty of Health and Public Services, Institute of Mental Health, Doctoral Division of Mental Health Sciences, Budapest, Hungary

Data-Driven Health Division of National Laboratory for Health Security, Health Services Management Training Centre, Semmelweis University, Budapest, Hungary

Beáta DÁVID
Semmelweis University, Faculty of Health and Public Services, Institute of Mental Health, Doctoral Division of Mental Health Sciences, Budapest, Hungary

HUN-REN Centre of Social Sciences, Institute for Sociology, Budapest, Hungary

DOI: https://doi.org/10.2478/orga-2025-0015

Abstract

Background and purpose: The purpose of this study, guided by the Job Demands-Resources Model, is to investigate the role of workplace friendships in mitigating burnout. This research is notable for its unique focus on a relatively rare sample: social care leaders. These individuals play a crucial role in shaping and influencing social services, making their insights invaluable for understanding the challenges and opportunities within this sector.
Methods: Using a cross-sectional and quantitative design, data were collected from a convenience sample of Hungarian social care leaders, including sociodemographic information, the Copenhagen Psychosocial Questionnaire (COPSOQ II), and professional core discussion network (pCDN) questions. The analysis of 449 Hungarian social care leaders employs a saturated model of moderated mediation (controlling for age and gender) to examine how stress mediates the relationship between quantitative demands and burnout and how workplace friendships moderate this mediation effect.
Results: The results indicate that stress significantly mediates the relationship between quantitative demands and burnout, with workplace friendships acting as a buffer under moderate stress levels. Having at least one workplace friend reduces the impact of stress on burnout; however, this protective effect diminishes under higher stress intensities.
Conclusions: These findings underscore the importance of fostering quality and balanced workplace friendships rather than merely increasing the number of supportive relationships. Given the systemic challenges in Hungarian social care, these insights are particularly relevant for leaders seeking to improve workforce resilience and well-being.

Keywords: Workplace friendship, Burnout, Stress, Quantitative demands, Social care leaders, Social work

Jana HOJNIK
University of Primorska, Faculty of Management, Koper, Slovenia

Simona KUSTEC
University of Primorska, Faculty of Management, Koper, Slovenia

DOI: https://doi.org/10.2478/orga-2025-0016

Abstract

Objective: To trace how academic discourse on social innovation has evolved from 2000 – mid-2024 in numbers and leading topics by applying a special topic modelling and text mining methodology.
Data & Sources: 4,703 full-text journal articles retrieved from Science Direct.
Methods: Literature review and PDF text extracted with PyPDF2 and pdfplumber; cleaned and tokenised in R; topic modelling performed with Latent Dirichlet Allocation (ldatuning-optimised); temporal and correlation analyses visualised via tidyverse. Results: The number of publications increased significantly from 16 (in 2000) to 573 (in 2021), stabilizing thereafter. Seven dominant topics emerged: renewable energy, environmental/resource management, smart-city governance, sustainable food systems, corporate strategy, academic-method studies, and social-governance structures. “Social” and “innovation” became the top word pair after 2006; energy-related terms surged after 2016. Surprisingly, topics typically considered ‘social’ have not dominated the social innovation discourse in scientific communities compared to the aforementioned dominant topics.
Discussion: Our results largely confirm existing findings from literature reviews and affirm the interdisciplinary, vague, contested, and still intensively evolving nature of social innovation. Dominant social innovation topics in scientific papers reference to social innovation topics in global political and policy documents, notably from the EU (from 2013 onwards) and the 2015 UN SDGs agenda, also emphasising collaboration between scientific, business, political and non-governmental stakeholders, and can thus serve as scientific, evidence-based advocacy for other stakeholders involved in social innovation processes.
Conclusions: Social innovation research is now an established, systemic, and broadly interdisciplinary field of study, focusing on sustainability, emerging technologies, and governance topics. It is tightly connected with the political and policy agendas of leading international organisations, as well as business and non-governmental ones.
Implications: Findings guide scholars to under-explored social-related content and niches (such as governance and, especially, equity topics) and help policymakers and other stakeholders involved in social innovation processes locate evidence-based approaches and clusters when designing their socially innovative responses, interventions, solutions, and measures.

Keywords: Social innovation theories, Global policy agenda, Text mining, Topic modelling literature review

Ahmet Melih EYİTMİŞ
Kahramanmaraş Sütçü İmam University, Faculty of Economics and Administrative Sciences, Department of Business Administration, Kahramanmaraş, Turkey

Muhammet Zeki ZÜMRÜT
Kahramanmaraş Sütçü İmam University, Institute of Social Sciences, Department of Business Administration, Kahramanmaraş, Turkey

DOI: https://doi.org/10.2478/orga-2025-0017

Abstract

Background and purpose: There is interest in barriers to change in organizations. This research discusses factors affecting resistance to change based on employees’ technological competencies. This research aims to determine the mediating role of technology readiness in the effect of organizational climate in health institutions on resistance to change.
Methodology: Research data were collected from 389 employees working in the healthcare sector. SPSS Process 2.13 macro was used to analyze the model.
Results: According to the analysis results, organizational climate positively affected technology readiness. Additionally, organizational climate reduced resistance to change. In addition, employees’ readiness for technology reduced resistance to change. Finally, the mediating role of technology readiness (motivating and blocking factors) in the effect of organizational climate on resistance to change was significant. Further, a positive organizational climate in healthcare institutions increased employees’ readiness for new technologies and significantly reduced employees’ resistance to change.
Conclusion: Creating a positive organizational climate can be vital in successfully implementing change processes in the healthcare sector. At the end of the research, theoretical and practical suggestions were presented. The research contributes to the literature by addressing the antecedents of resistance to change from organizational and individual perspectives.

Keywords: Healthcare workers, Organizational climate, Resistance to change, Technology readiness

Jaumin AJDARI
South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia

DOI: https://doi.org/10.2478/orga-2025-0018

Abstract

Background/Purpose: The integration of machine learning in education has opened new possibilities for predicting student performance and enabling early interventions. While most of the work has been focused on prediction algorithms design and evaluations, little work has been done on user-centric evaluations.
Methodology: This study evaluates a web-based platform designed for student performance prediction using various machine learning algorithms. Users, including students, professors, and career counselors, tested the platform and provided feedback on usability, accuracy, and recommendation likelihood.
Results: Results indicate that the platform is user-friendly, requires minimal technical support, and delivers reliable predictions.
Conclusion: Users strongly endorsed its adoption, highlighting its potential to assist educators in identifying at-risk students and improving academic outcomes.

Keywords: Student performance, Machine learning, System evaluation