Artur Strzelecki

The rapid proliferation of generative artificial intelligence (AI) tools, particularly large language models such as ChatGPT, has fundamentally transformed the landscape of higher education. This study investigates the factors influencing the adoption of generative AI by university students through the theoretical lens of Diffusion of Innovations (DOI) theory. Drawing upon Rogers’ seminal framework, this research examines how five perceived attributes of innovation-relative advantage, compatibility, simplicity, trialability, and observability-is related to students’ behavioral intention to use generative AI tools in their academic pursuits. A cross-sectional online survey was conducted among students from multiple Polish universities, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated satisfactory psychometric properties, with all constructs achieving acceptable levels of internal consistency reliability, convergent validity, and discriminant validity. The structural model results indicate that relative advantage, simplicity, observability and compatibility are significantly related to behavioral intention, while trialability demonstrates a non-significant effect. The model explains approximately 48.7% of variance in behavioral intention. These findings contribute to the theoretical understanding of technology adoption in educational contexts and provide practical implications for higher education institutions seeking to integrate generative AI tools into their pedagogical frameworks. The study concludes with recommendations for fostering responsible AI adoption while addressing concerns regarding academic integrity and critical thinking development.

This study examines ChatGPT adoption among higher education students in Türkiye and Poland through an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework that also includes personal innovativeness. It compares country-specific adoption patterns across the two samples and offers a cautious contextual interpretation of the observed differences. Survey data were collected from 634 students in Türkiye and 531 students in Poland, and the proposed relationships were examined using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that the extended UTAUT2 framework was empirically useful in both samples, although the relative prominence of specific predictors differed across them. Descriptively, Performance Expectancy (β = 0.258, p < 0.001) and Personal Innovativeness showed larger coefficients in the Polish sample, whereas Effort Expectancy (β = 0.104, p < 0.01) and Social Influence (β = 0.199, p < 0.001) appeared more prominent in the Turkish sample. Price Value showed a statistically significant reversed association with Behavioral Intention in Türkiye (β = -0.187, p < 0.001), whereas the association was positive in Poland (β = 0.094, p < 0.01). Habit emerged as one of the strongest predictors in the model and was positively associated with both Behavioral Intention and Use Behavior in the two samples. Taken together, the findings suggest that ChatGPT adoption in higher education is shaped by a combination of perceived usefulness, ease of use, social and practical support, habitual use, and context-sensitive evaluations of value. The study contributes to the growing literature on generative AI in higher education by showing that the same theoretical framework can reveal different adoption patterns across two national samples, while also highlighting the need for context-sensitive implementation strategies. The findings offer practical implications for higher education institutions, educators, and policy developers seeking to support the responsible and effective use of ChatGPT.

Responsible consumption and production, emphasized in Sustainable Development Goal 12 (SDG 12), require both company‐level sustainability initiatives and an understanding of how consumers respond to corporate responsibility signals. Environmental, Social, and Governance (ESG) framework has become a central mechanism through which companies communicate their commitment to sustainable development; however, the behavioral pathways that link ESG perceptions with consumer behavior remain under‐researched. This study examines how consumers' perceptions of companies' ESG engagement shape sustainable consumption through the mechanisms proposed by the Theory of Planned Behavior (TPB). Using survey data from a nationwide sample of 1000 consumers across four generational cohorts, the model is tested with partial least squares structural equation modeling and multigroup analysis. The results indicate that perceived ESG engagement acts as a structured antecedent that influences attitudes, subjective norms, and perceived behavioral control, which subsequently shape behavioral intention and actual sustainable consumption. Attitudes emerge as the dominant mechanism, indicating that ESG‐aligned consumption is largely value‐driven, while normative and control‐related factors play complementary roles. Although behavioral intention significantly predicts actual behavior, an intention–behavior gap remains. Generational differences are limited and mainly concern the translation of intention into behavior. By integrating ESG into TPB, the study provides a behavioral micro‐level explanation of how company‐level ESG engagement supports the objectives of SDG 12.

Digitalization and artificial intelligence (AI) are seen as promising pathways for small and medium-sized enterprises (SMEs) to enhance performance while preserving environmental and social resources. This paper identifies organizational determinants of AI maturity that can enable SMEs to use AI in a more sustainable, responsible, and capacity-enhancing manner. AI adoption becomes relevant to sustainability not only because a company adopts advanced technology but because this technology is embedded in leadership practices, employee competencies, interdisciplinary collaboration, and organizational learning. From this perspective, perceived benefits and management commitment are not outcomes of sustainability but mechanisms that help explain how SMEs transition from technological awareness to building organizational capacity. Such capacity building can be a necessary prerequisite for subsequent sustainability-oriented outcomes, such as efficient resource utilization, employee upskilling, responsible AI management, and long-term resilience. We conducted a cross-sectional survey among 402 managers from Polish SMEs (62 micro, 193 small, 147 medium) across manufacturing, services and trade industries. Respondents (mean age ≈ 42.5 years) assessed perceived benefits of AI, engagement of top leadership, AI maturity and competitive pressure. Partial least-squares structural equation modeling revealed that perceived benefits strongly predicted leadership engagement (β = 0.647), explaining 62.8% of its variance. Perceived benefits (β = 0.384) and leadership engagement (β = 0.362) in turn were the key drivers of AI maturity, with the model accounting for 65.5% of variance in AI maturity. Competitive pressure positively but weakly moderated the relationship between perceived benefits and leadership engagement (β = 0.011), while its moderating effect on the relationship between perceived benefits and AI maturity was not significant (β = −0.008). These findings suggest that articulating clear benefits of AI and securing active leadership engagement are more decisive for advancing AI maturity than external competitive pressure. The contribution of the study is to integrate the perceived benefits of AI, top management commitment and AI maturity into a model, empirically validated and interpreted from a socio-technical perspective of sustainable digital transformation in SMEs, while quantifying the moderating role of competitive pressure in the under-researched context of Central and Eastern Europe. For practitioners, investing in awareness of AI’s benefits and developing committed leadership may yield more sustainable digital transformation than reacting solely to external pressures.

The adoption of educational technologies has become pivotal for improving the teaching and learning process in higher education. However, in regions like Uttarakhand, which are in general, considered to have institutional and geographical diversity, the determinants influencing the adoption of educational technologies among the faculty members remain insufficiently explored. The present study aims to investigate the critical factors affecting the acceptance and use of educational technologies among university teachers through the approach of extending the existing UTAUT2 model. As an extension purpose, two additional constructs Personal Innovativeness and Value Belief which have been identify through the literature review have incorporated to reflect the local academic context, while one of the key construct of the existing model i.e. Price Value was excluded due to its limited relevance in the context of teachers as stakeholders. A quantitative approach was followed by analysing the data collected from the 389 university teachers in Uttarakhand. Findings disclose that constructs such as Facilitating Conditions, Performance Expectancy, and Effort Expectancy are the key significant constructs which significantly influenced Behavioural Intention and Use Behaviour. Further, it was observed that Personal Innovativeness had a strong influence on intention but not on actual use, which indicates a gap between motivation and implementation. Among all, two of the main constructs viz. Hedonic Motivation and Habit were not found significant in case of university teachers in Uttarakhand, and hence highlights the role of cultural and infrastructural factors. Finally, the proposed model shows the explanatory power (R ² = 0.772) which is high in comparison to the existing models such as UTAUT2 and UTAUT3 in the given content. At the end, limitations and directions for future research are also discussed along with future implications.

The aim of this study was to examine the impact of the TikTok platform on shaping the dietary preferences of young people. The research focused on analyzing the mechanisms through which social media influence consumer behavior in the area of nutrition, which is a particularly important issue in the context of the growing popularity of digital platforms and their effect on everyday decision‐making. The study employed an individual model of technology acceptance and use that included eight analytical dimensions: perceived usefulness, entertainment content, virality, influencer review, social influence, subjective bond, attitude, and food behaviors. Data were collected through an online survey conducted in 2025 among 406 active TikTok users. The analysis was carried out using SmartPLS4 and structural equation modeling. The results confirm that TikTok functions as a powerful tool for shaping consumer behavior, utilizing algorithmic mechanisms, social influence, and users' emotional engagement. The most influential external factors are entertainment content, followed by subjective bond and perceived usefulness. Based on the estimation, the variance of attitude towards culinary trends was explained in 68.8% and food behaviors was explained in 54.5%. The platform affects dietary preferences through various channels of influence, ranging from culinary inspiration to the development of bonds with content creators.