«Личность: ресурсы и потенциал» | Выпуски журнала

This article analyzes the cross-cultural determinants of trust in artificial intelligence systems within Russian and Southeast Asian socio-cultural contexts. The study aims to identify the socio-psychological mechanisms underlying the formation of trust in artificial intelligence and to examine their cultural conditionality. The methodological framework includes theoretical analysis, systematization, and synthesis of academic literature, as well as a comparative analysis of approaches to trust within human-AI interaction using G. Hofstede’s cultural dimensions theory and F. Davis’s Extended Technology Acceptance Model (TAM2/TAM3). The research identifies two dominant trust patterns: a functional-pragmatic pattern (Russia) and a relational-harmonizing pattern (Southeast Asian countries), which differ regarding the roles of uncertainty, institutional factors, and social norms. Furthermore, the concept of «cultural calibration of algorithms» is proposed. The theoretical significance of the study lies in advancing the cross-cultural approach to technology trust, while its practical value stems from the application of the findings in developing culturally adaptive artificial intelligence systems.

This article analyzes the impact of artificial intelligence on the human sense of agency during decision-making processes. The study aims to conceptualize the transformation of the sense of agency within human-AI interaction and to identify the factors that contribute to either preserving or diminishing the experience of authorship. The methodological framework encompasses theoretical analysis, systematization, and synthesis of domestic and international research in the psychology of agency, cognitive psychology, and human-computer interaction. The findings demonstrate that artificial intelligence redistributes cognitive functions, thereby altering the perception of control and accountability. Furthermore, the article analyzes the effects of automation bias, reduced reflexivity, and the erosion of accountability. The study establishes that the degree of agency transformation depends on algorithmic transparency, the explainability of decisions, and the opportunity for user intervention. The theoretical significance of this research lies in refining the conceptual understanding of distributed agency within digital environments, while its practical value stems from establishing design principles for artificial intelligence systems that support human agency.

This article examines why digitalization often yields limited improvements in public sector governance. It argues that implementing digital technologies fails to automatically enhance organizational control if the document-centric workflow typical of traditional bureaucracy remains unchanged. Transitioning to genuinely data-driven governance requires a fundamental shift not only in information systems but also in how managerial data is conceptualized and structured. Central to this transition is data modeling, which maps administrative activities into interconnected networks of events, roles, states, and outcomes. This approach shifts the operational focus from mere document processing to comprehensive informational support across the entire management cycle. Additionally, the study explores the workforce implications of this digital shift. Structural changes in the information environment demand new competencies from public sector executives and specialists, specifically in data literacy, decision parameterization, and data governance. Ultimately, the practical value of this research lies in its framework for designing data-driven public administration systems and establishing updated competency models for civil servants.

This article addresses the current challenges surrounding the integration of Russian education, science, and industry practice. It examines global trends, including accelerated technological development, the imperative for technological sovereignty, the multidisciplinary nature of modern knowledge, and the shortage of qualified personnel. The study analyzes the shortcomings of the existing system, such as the protracted specialist training cycle, the misalignment between professional and educational standards and market demands or technological trends, and the insufficient link between academic education and the realities of practical organizational workflow. The findings demonstrate that flexibility and differentiation must become the cornerstone directions for addressing these issues. Finally, the author proposes measures to enhance the flexibility and efficiency of the educational system, including a shift away from rigid standardization, the expansion of employer engagement, and a transition from quantitative metrics to qualitative evaluation criteria.

This article explores the role of public commitments as a management tool amidst the widespread adoption of project-based work structures and hybrid employment models. The study aims to substantiate the necessity of transitioning from a paradigm of individual discipline to the design of social control systems. The methodological framework comprises an analysis of performance discipline within project teams, action research, and direct observation of task dynamics across more than 30 sprints between 2022 and 2024. The findings indicate that when transparency and consequence significance are maintained, the share of on-time task completion in the observed teams reached 85–90%, compared to 60–70% under traditional planning approaches. The theoretical significance of this research lies in advancing the understanding of social facilitation as applied to project team management and introducing the concept of the «neighbor effect»—the author’s interpretation of this phenomenon within the context of hybrid employment. The practical value stems from the utility of the developed tool for project managers and consultants, enabling them to optimize processes without resorting to authoritarian practices.

This article examines the paradox of algorithmic legitimacy, defined as the contradiction between the growing trust in artificial intelligence for managerial decision-making and the erosion of accountability for the outcomes of such decisions. The study characterizes typical scenarios of managerial failures when utilizing algorithmic systems in commerce, finance, human resource management, logistics, and public administration. Furthermore, it uncovers the root causes of accountability erosion, specifically focusing on automation bias, the opacity of algorithmic models, and the lack of institutional control mechanisms. The author describes an «accountability architecture» model that integrates technical, institutional, legal, organizational, and educational levels of control. Additionally, a classification of artificial intelligence systems based on their level of autonomy is proposed, and corresponding control mechanisms are defined. The study concludes with the necessity of transitioning from declarative ethical principles to mechanisms of distributed control.

This article examines a corporate recognition digital application as a pivotal mediator linking employees’ everyday practices, the corporate values transmitted by the application, and organizational managerial processes. Grounded in Actor-Network Theory (ANT), this study traces how the application, functioning as a technical artifact, is integrated into a network of relations alongside humans, regulations, and metrics, and how this network shapes interaction processes among users. Utilizing empirical data, the paper analyzes, on the one hand, the integration process of the application into the daily routines of its users (specifically, shifts in interaction pathways and forms of recognition) and, on the other hand, the application’s impact on the configuration of the gratitude network among these employees. The findings demonstrate that capturing micro-practices within a digital system renders them visible and manageable for executives. This visibility enables highly targeted managerial interventions and facilitates a paradigm shift toward data-driven management.