Business: Theory and Practice

This study investigates how students’ perceptions of the value and meaning of learning translate into sustainable and prosocial behavior through competence/autonomy and moral orientation. Cross-sectional survey data were collected from 417 university students, and mean composite scores were computed for all variables (a pragmatic approach given the survey design; latent measurement modeling was beyond the scope of the present analysis). A serial mediation model (PROCESS-type, Model 6) with standardized variables and 1,000 bootstrap resamples was estimated, controlling for religiosity/spirituality, course delivery mode, and volunteering hours. Learning value strongly predicted competence/autonomy (β = .71, R² = .51), which subsequently predicted moral orientation (β = .49). In the final model, competence/autonomy remained a significant predictor of sustainable behavior (β = .28, p < .001; model R² = .18), whereas moral orientation was not statistically significant (β = .08, p = .247). The indirect effect of learning value on sustainable behavior via competence/autonomy was substantial (β = .20), while the serial pathway through both mediators was small and non-significant. Most of the total effect of learning value (β = .36) was indirect. Although the cross-sectional and single-country design limits causal inference and generalizability, the findings highlight competence-building and autonomy-supportive learning designs as key mechanisms for transforming meaningful learning into sustainable and ethics-aligned actions.

This study aims to provide a comprehensive synthesis of existing empirical research on employee attitudes toward artificial intelligence (AI) in the workplace, with a particular focus on how these attitudes are defined and measured. Therefore, systematic literature review was performed. Following PRISMA guidelines, search conducted in two databases, EBSCOhost and Scopus, yielded 642 records, of which 29 met the inclusion criteria. The included studies, published between 2021–2025, covered a broad geographic and sectoral range, encompassing the United States, Europe, Asia, the Middle East, and involved various occupational groups, most commonly general employees, managers, and HR professionals. Review findings indicate that employee attitudes are conceptualized as a multi-dimensional phenomenon, most commonly situated within a cognitive–affective framework. However, the methods used to measure attitudes vary widely, with studies drawing on a combination of established models, adapted scales, and context-specific instruments, which in turn restricts the comparability of results across studies. Drawing from human-centered perspective, this study is timely in highlighting the need to clearly define fundamental employee attitudes toward AI, to employ a validated method to measure them, thereby enabling comparability in future research, and supporting organizations to implement and use AI effectively.

This research investigates the extent to which the essential aspects of digital capabilities like Big Data Analytics (BDA), Digital Competence (DCT), and Quality of Information Technology (QIT) influence Strategic Management Accounting (SMA) and Competitive Advantage (CA) in Vietnamese tourism businesses. Data were gathered from 474 tourism businesses in Ho Chi Minh City, Vietnam, and analyzed using PLS-SEM. The results indicate, the three elements of digital capability have significant positive impacts on both SMA and CA, while SMA itself exerts a strong positive impact on CA. Furthermore, DCT serves as the strongest determinant of SMA. Additionally, mediating analyses indicate, SMA mediates the relationships between digital capabilities and CA, suggesting digital capabilities are a greater source of competitive returns when they are integrated into SMA. The results underline SMA as an important process for turning digital capability into better performance. This research contributes to the current literature by integrating digital capabilities and SMA in an investigation of the tourism industry. It presents a capability-to-advantage chain from BDA/DCT/QIT → SMA → CA. In practical terms, the results suggest that Vietnamese tourism businesses should invest in BDA, DCT, and IT infrastructure and institutionalize SMA routines to sustain CA in an increasingly digitized era.

Research on customer experience in B2B services remains fragmented, with existing studies typically examining isolated elements and offering limited insight into how customer experience is structured as a whole. The present study addresses this gap through a systematic literature review conducted under PRISMA guidelines. A comprehensive review of 21 peer-reviewed studies was conducted to identify constructs of customer experience in business-to-business (B2B) services. Through rigorous analytical organization, these constructs have been categorized into five interconnected groupings: antecedents, components, consequences, mediators, and moderators. The study’s principal contribution is the development of an evidence-based integrative map that synthesises these categories and visualizes their interdependencies, conceptualizing B2B customer experience as an interconnected phenomenon rather than a set of independent factors. The findings are presented through an explicit integrative map and a simplified structural representation, offering fidelity to existing research and analytical clarity. By revealing conceptual overlaps, missing links, and inconsistent use of constructs across prior research, the study clarifies internal structure and boundaries of the B2B customer experience construct. The findings provide a structured foundation for future empirical research and support a more coherent approach to managing customer experience in B2B service contexts.

In the modern era, digital transformation has become an integral part of the development of economies and society as a whole. Digital technologies, such as automation, artificial intelligence, the Internet of Things (IoT), and analytical platforms, have great potential for optimizing economic processes, increasing productivity, and developing innovations. However, the implementation of digital technologies brings with it a number of challenges, including the need to adapt to the specific needs of different industries and effectively manage these processes. The purpose of this article is to develop an integrated algorithmic model for optimizing digital transformation aimed at maximizing economic growth and innovation in different industries. Special attention is paid to the development of mathematical models, in particular genetic algorithms and multi-criteria optimization methods, to predict the impact of digitalization on productivity, costs, and the innovative potential of enterprises. The article offers a comprehensive approach to assessing the economic effect of implementing digital solutions, in particular, through simulation modeling, which allows taking into account numerous factors affecting digital transformation, such as the development of digital infrastructure, the level of innovation and the adaptation of technologies to the specifics of each industry. Thus, the article not only offers a new methodology for optimizing digital transformation, but also contributes to the formation of tools for assessing and strategically managing digital solutions at the enterprise and state levels. The developed model can be an important step in improving digitalization strategies to increase efficiency and competitiveness in the face of globalization and technological change.