Miglena Angelova

The focus of this report is on exploring the relationship between algorithmic management and the specifics of high-growth firms, emphasizing the mechanisms through which data and algorithms support the achievement of accelerated organizational development. The analysis examines the role of artificial intelligence in decision-making processes, resource optimization, and adaptation to a dynamic business environment. On this basis, the main management mechanisms through which algorithmic management can function as a growth factor are identified, as well as limitations related to data dependence, transparency, and ethical aspects of algorithmic management.

The use of Artificial Intelligence (AI) has become an inseparable part of the software development process, parts of which are routine, requiring precision rather than creativity. AI code generation speeds up productivity by automatically completing code fragments and frees up time and brainpower to perform more tasks that require creativity or problem-solving. When writing code, the programmer uses not only the accumulated experience, but often also intuition, which helps him assess both the correctness of the code and the implications of its use. At the same time, AI uses different and increasingly sophisticated models and a thought chain resembling human thinking through modern approaches to Large Language Models (LLM) and Neural Networks. However, increasingly serious problems are emerging when using AI. The publication proposes a model of an AI algorithm with two functionally different cores: one creative and one process, including control functions. This model overcomes a large part of the identified shortcomings of AI and complements their development. The model is supplemented with quantitative metrics to determine the economic efficiency of its application.

Background. Modern logistics is impossible without the construction and development of intermodal logistics hubs, the effectiveness of which largely depends on the level of digitalisation and the quality of operational coordination, which are key factors in their competitiveness. Purpose. This study aims to analyse the role and importance of modern intermodal logistics terminals in achieving efficiency in the integration of transport networks and to evaluate the impact of digitalisation and organisational coordination on the efficiency of intermodality in Bulgaria. Findings. The analysis revealed average terminal efficiency, primarily due to insufficient coordination among supply chain participants and increased cargo idle time. Infrastructure limitations are less critical than partial digitalisation and the lack of comprehensive digital solutions. The overall efficiency of intermodal terminals in Bulgaria is rated as average, with 47% of respondents rating it only as “satisfactory”. Weak coordination between operators (66% of participants) and insufficient digital integration were cited as key issues. The latter point is supported by the fact that 69% of specialists rated the current level of digitalisation as average or low, and 63% consider its improvement a key development priority. The lack of coordination and digital solutions directly correlates with operational losses, as cargo dwell times reach 3-6 hours; however, with better organisation, this can be reduced to 1-3 hours. According to 82% of respondents, these delays lead to increased logistics costs. The results confirm that the development of digitalisation is the foundation for improved coordination and operational efficiency, making it a key factor in optimisation. Implications. Digital integration is emerging as a strategically more important factor in the current development of intermodal terminals, and skilled integrated information management, especially against the backdrop of the country's strategic geographical location, will lead to rapid opportunities to improve the overall efficiency of intermodal terminals. Standardisation of electronic customer relationship management (e-CRM) documents and the use of full automation capabilities, including optical character recognition (OCR), radio-frequency identification (RFID), and terminal operating system (TOS) integration, are also recommended for future studies.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Introduction. With the development of digital technologies and the increasing penetration of artificial intelligence into business, algorithmic management is becoming an integral part of overall organisational management. This study, based on a qualitative analysis of logistics, finance, IT services, and manufacturing organisations, contributes to the understanding of algorithmic management in the development of digital transformation by examining the motivations of workers from different sectors of the economy. Aim and tasks. This study focuses on introducing and using algorithmic management as a means of successful digital transformation in an organisation. The main aim is to explore the experience of specific business organisations operating in different sectors of the economy regarding the introduction and use of algorithmic management. Results. The decision to implement algorithmic management is complex, influenced by both internal and external factors, including competitors and technological advancements. Social and ethical aspects relate to efficiency, faster decision-making, meeting customer needs, and fostering innovation. The impact on employees can be defined as two-way: algorithmic management presents an opportunity for better development, but it is also associated with direct concerns about job loss, changes in established work habits, invasion of personal space, and excessive control. Regardless of an organisation’s field of work, ethical dimensions are always present and are perceived as a significant opportunity to overcome the negative consequences of implementing algorithmic management. The consequences reveal differences between sectors: for logistics, the emphasis is on the speed of service; in finance, on transparency and risk management; in the IT sector, on innovation; and in manufacturing, on process optimisation and cost reduction. Conclusions. In order to be successfully and sustainably implemented, algorithmic management should be part of an overall strategy for the organisation’s development in the context of digital transformation. It is recommended that organisations introduce a phased approach to algorithmic management, with parallel training for employees, the introduction of mandatory mechanisms for transparency in decision-making and uncompromising standards for personal data protection.

With the spread of digital technologies and mobile applications, a new way of working has emerged - the so-called “gig economy”. In it, people work through online platforms (such as Uber, Glovo, etc.), performing short tasks for pay. They are not employees in the traditional sense, but rather selfemployed individuals who are bound by the rules of the platforms. These platforms use algorithms that automatically distribute tasks, monitor the performance of workers and even decide who to remove from work. This type of management, called “algorithmic management”, is based entirely on modern information and communication infrastructure - cloud services, GPS, mobile applications and large data sets (Big Data). The study examines both the benefits (such as speed, efficiency and automation) and the serious ethical and legal challenges of this model. Topics such as lack of transparency in algorithmic decisions, violated labor rights, digital surveillance and the inability to protect against unlawful actions by the system come to the fore. The research emphasizes the need to balance technological efficiency with the protection of human rights. Good practices are proposed, such as including the human factor in important decisions and using transparent and open algorithms to avoid unethical treatment of gig economy workers.

This article examines the ethical and legal aspects of algorithmic management, which, with the development of digitalization and the introduction of artificial intelligence into many business processes, is becoming an increasingly essential part of our daily professional lives. It analyzes both the possible threats of algorithmic management, mainly related to lack of transparency, opacity, excessive intrusion into personal space and hence - the violation of fundamental human and labor rights, and the advantages that ICT can offer in labor aspect - mainly facilitated access to legal information, online consultations, etc. The main regulatory acts in the European context - GDPR and the AI regulation - are discussed. Several important aspects that could lead to the improved transparency and reliability of the algorithmic management are also considered.

Introduction. Digital transformation has become a determinant factor influencing enterprises’ economic security, especially during crises and global concurrences. Despite its numerous advantages, digitalisation poses new risks, such as cyber threats, regulatory instability, and information leaks. Digitalisation as an infrastructure for innovation has become a strategic priority, primarily through the extension of digital government services, cloud resolutions, and metadata analytical tools to support the economic development of industry enterprises. Aim and tasks. This study aims to develop an integrated system of strategic risk management for digital transformation in industry enterprises’ economic security to minimise economic losses, increase adaptiveness to digital changes, and eliminate risks in industry enterprises’ economic security. Results. This study analyses modern approaches to ensuring economic security: systemic, risk-oriented, resource-oriented, and integrated approaches. The analysis revealed that only 25.6% of IT enterprises utilise Big Data analytics, while 7.1% employ AI technologies, highlighting the substantial untapped potential for further digital transformation. This study developed a structural model of a digital economic security management platform, including risk analytics, threat management, cyber protection, monitoring, and reporting. A formalised risk estimation model was built, considering financial, operational, and external factors. An integrated approach is suggested to build a digital platform for the management of enterprises’ economic security, which combines risk-oriented methods, cyber-protection, and big data analytics. Conclusions. This study proposes an approach for digital transformation in economic security management, featuring module architecture to monitor, forecast, and respond to threats. It presents formalised module architecture and a platform structural model for economic security monitoring, comprising four subsystems: financial, operational, risk-oriented, and external factors. The model enables rational resource allocation, threat forecasting, and adaptive decision-making in post-conflict scenarios. Enterprise digital transformation in strategic management and economic security is a key requirement for restoring, strengthening, and safeguarding the national interest.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies