Purpose: To enhance enterprise efficiency, this study examines the pivotal role of operations management in optimizing the use of materials, technology, equipment, and personnel, especially within the transformative framework of Industry 4.0.Design/methodology/approach: This article investigates operational preparation in the context of Industry 4.0. Through questionnaires and interviews with stakeholders, problems related to operational preparation were identified. To address these problems, a launching model was developed for production systems. The model considers several parameters important for the production process, including business goals, customer needs, and environmental conditions that impact enterprise performance and profit.Findings: The model determines the importance of jobs to be performed in a certain order to optimize production sequence. The proposed parameters are included in a mathematical-algorithmic launching model based on categorical levels related to the production process, such as profit, delivery times, processing times, total number of technological operations, product types, materials, required quality, product complexity, and resource use. The model was developed based on observations and investigations conducted in Kosovo's enterprises on operations research, and it has the potential to significantly improve production efficiency and profitability in the Industry 4.0 era.Practical implications: The findings of this study have practical implications for operations management within the Industry 4.0 framework, proposing a launching model designed to optimize production processes and enhance efficiency.Originality/value: The originality and value of this research lie in the development of a mathematical-algorithmic launching model that addresses operational preparation in the Industry 4.0 context, taking into account various crucial parameters.

Fintech's transformative impact on financial services necessitates a nuanced exploration of its implications, reshaping how individuals and businesses manage their financial resources. However, effectively utilizing these innovations demands a certain level of financial proficiency. This study explores the complex connection between financial literacy and financial technology adoption in the context of the Kosovo region, utilizing two distinct measures of financial literacy. Data from a cross-sectional survey of 187 respondents undergo Probit regression analysis, emphasizing mobile banking, mobile payments, and digital lending. The results reveal a positive correlation between individuals perceived financial literacy and adoption of all 3 fintech services, underlining the significance of augmenting self-perceived financial capabilities for wider fintech adoption. In contrast, objectively gauged financial literacy positively correlates exclusively with mobile banking adoption. The study unravels demographic nuances as pivotal factors influencing the varied landscape of Fintech adoption. These findings provide actionable insights for policymakers to design targeted financial education programs that build confidence in fintech usage. Fintech providers should enhance platform usability and trust, while educators can integrate fintech content into curricula to prepare future users. By addressing demographic-specific barriers, stakeholders can foster inclusive fintech ecosystems. Furthermore, the results wield significance for policymakers and Fintech stakeholders, emphasizing the imperative of augmenting individuals' subjective confidence in their financial capabilities to propel widespread Fintech assimilation. This research provides a distinctive lens into the intricate interplay of financial literacy and Fintech integration, contributing substantively to the evolving narrative in this domain.

This study examines the impact of artificial intelligence (AI) on human resource management (HRM) practices in Kosovo’s small and medium–sized enterprises (SMEs), focusing on outcomes such as accuracy, automation, computing power, and real-time analytics, to understand their effects on efficiency and cost reduction. The research employs a quantitative approach, utilizing data collected from 109 SMEs through structured surveys and in-depth interviews. Structural equation modeling (SEM) was used to analyze the relationships between AI capabilities and HRM efficiency outcomes. The results reveal that AI-driven accuracy and automation significantly enhance time efficiency and cost reduction, while real-time analytics has a moderate influence, and computational power has a limited impact within the SME context assessed. The findings suggest that SMEs should prioritize accuracy and automation tools to achieve immediate operational benefits and gradually integrate real-time analytics as the digital infrastructure improves. This study provides practical insights for SMEs on how to implement AI strategically in HRM processes, demonstrating that AI serves as an essential complement to human expertise rather than a replacement, offering a clear pathway for enhancing HR practices in developing economies.