This paper investigates the impact of artificial intelligence (AI) on the forklift industry, focusing on logistics and procurement within small and medium-sized enterprises (SMEs) in Romania. Using a mixed-methods approach, including interviews with seven managers from a benchmarked company in the forklift industry (BCFI) and quantitative analysis of operational data, we examine the transformative effects of AI integration. Key findings include a 30% reduction in inventory holding costs due to AI-powered predictive analytics; a 15% decrease in procurement costs through AI-driven supplier evaluation systems; a 25% increase in operational efficiency from AI-optimized route planning; a 40% boost in overall productivity attributed to AI-enabled automation; and a projected 20% reduction in low-skilled labor requirements over the next five years. The study employs environmental, social, and corporate governance (ESG), balanced scorecard (BSC), benchmarking, and activity-based management (ABM) models to analyze risks and implications of AI integration. A case study of a leading Romanian SME in the forklift industry is presented, examining financial strategies using McKinsey’s 7S framework. The paper concludes that while AI offers significant operational benefits, it also presents challenges in workforce transition and ethical considerations that require careful management.

Introduction. European social economy employment is 6.3% of the European Union's (EU) working population. In Romania and other recent EU member states, it is responsible for less than 2% of the total employment. The need for a precise legal and political concept of social economy and social enterprise confused the Romanian population and made their measurement easier. Both concepts have been increasingly employed in Romania since 2005, following the new EU policy approaches for social inclusion. Consequently, the social economy has grown significantly in recent years, with a diverse range of actors pursuing various social goals. Aim and tasks. The paper aims to provide an up-to-date overview of the social economy in Romania by showing the activity types and their involvement in the circular economy transition and then by examining the social enterprises' social and economic performance. Results. The creation of social value and social impact of social enterprises, the response of organisations to entrepreneurial change, and the level of risk-taking were substantiated. The way these aspects are correlated with performance was determined. A quantitative analysis of the social economy and transition towards a circular economy in Romania yielded the following main findings: i) more than half of the surveyed enterprises did not understand the concept of a circular economy, and ii) there was a strong statistically significant correlation (R=0.61) between social performance and environmental receptivity. The correlation between social and economic performance is statistically significant, with an average intensity (R=0.32). The correlation between economic performance and responsiveness to the environment was also statistically significant for medium-to-high intensity (R= 0.28). Conclusions. The social economy puts social and democratic objectives into practice, whereas a circular economy is essential for a sustainable transition. Various actors operate in different industries and geographic areas in the Romanian social economy. Social economy actors aim to contribute to a more inclusive economy and equitable society, inspiring the government and private sector to demonstrate and implement more inclusive and sustainable practices. These practices cover decent labour, more education, and secure lives. Social innovation, defined as long-term solutions to social problems, is a critical way to build a circular society.

Background: Despite significant technological advances, many logistics organizations in emerging markets struggle to realize the transformative potential of artificial intelligence, with reported success rates below 65% and limited theoretical understanding of the organizational capabilities. This study develops and proposes an integrated theoretical framework examining how knowledge process capabilities and dynamic capabilities interact to enable successful artificial intelligence adoption in logistics organizations within emerging market contexts. Methods: Through comprehensive literature review and theoretical synthesis, we propose a hybrid capability framework that integrates knowledge-based view perspectives with dynamic capabilities theory. Results: Theoretical analysis suggests that knowledge combination capabilities may be the strongest predictor of artificial intelligence implementation success, while dynamic reconfiguring capabilities could mediate the relationship between artificial intelligence adoption and performance outcomes. The proposed framework indicates that organizations with hybrid capability architecture may achieve superior implementation success compared to traditional approaches. Environmental uncertainty is theorized to strengthen the knowledge process capabilities—artificial intelligence adoption relationship. Conclusions: The framework suggests that successful artificial intelligence integration requires simultaneous development of knowledge-based and adaptive capabilities rather than sequential capability building. The hybrid capability framework provides theoretical guidance for managers in emerging markets, while highlighting the critical role of environmental context in shaping transformation strategies.

The aim of this paper is to analyze the physiognomy of unique sets in the maximum number of repetitions (MNR) strategy and different correlations between the maximal forces, duration and volume for a relevant exercise in the case of a small muscle group. The research methodology proposes testing, in two phases, a total of 30 male students, for bicep curl exercises carried out on a bicep Scott machine. The obtained results showed that there were significant differences between the maximum forces (Fmax) developed during the initial and final repetitions of the exercise sets or for different machine loads. There was a large correlation between the load and Fmax and an inverse correlation between the load and MNR or between the MNR and Fmax. The deterioration of the execution mode, represented by the profile of the final repetition of high-duration sets, was also tested and analyzed. We concluded that the study of the physiognomy of cycles and comparisons at the level of relevant repetitions have revealed new perspectives for the design of periodization strategies, for the possibility of manipulating adapted muscular response or compensatory acceleration training for small muscle groups or the MNR strategy.

The aim of this paper is to identify the possibilities of the implementation of the Innovative Management Framework for Intelligent Horticulture (IMFIH) by farmers with the aim of deepening the dynamics of innovation and technologic transfer processes related to the integration of the aerial work capability offered by mini UAV systems in precision horticulture. Starting from an aerial system for intelligent monitoring and smart horticulture applications, the research methodology is designed to understand the specific processes of this transfer of innovation in a field characterized by evolutionary dynamics and in the context of a lack of data. Thus, it is considered to be a mix of both quantitative and qualitative methods used in order to identify the needs and opinions of farmers regarding the possible use of the capabilities of mini UAV systems and especially how to access this capability. The obtained results showed the profile of the farmers interested in mini UAV systems for monitoring field crops and also the relevant factors for initiating/accessing them: specialized education, entrepreneurial education, area owned, ability to create partnerships, intention to access/develop mini UAV systems, and the existence of an integrated framework for analyzing the opportunities and restrictions of implementing mini UAV systems in precision horticulture applications. The integration of the Hype Cycle Paradigm (HCP) into the proposal of IMFIH led to the creation of the IMFIH-HCP as an innovative framework capable of stimulating the dissemination and transfer of knowledge and technology in the case of future horticultural applications of precision in an emerging market.

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment.