## 5 Results and Discussion
This may stem from implementation challenges at the operational level or from the fact that organizational outcomes tend to be more dispersed and harder to evaluate objectively. The ranking of the most important technologies for U2 is as follows: Cybersecurity (L12), Big Data and Data Analytics (L22), AI Combined with Human Intelligence (L45), Ethical AI and Social Responsibility (L44), Artificial Intelligence AI and Machine Learning ML (L21), and Augmented and Virtual Reality AR/VR (L34). This hierarchy of perspectives and corresponding technologies provide practical guidance for decision-makers in planning implementation priorities and identifying areas requiring further technological or organizational support. The observed precedence of the economic perspective (U1) over technological (U3) and organizational (U2) factors stems from expert evaluations rather than the model's inherent structure. Experts assessed the relevance of each perspective in the context of industrial transformation, resulting in the ordering U1 > U3 > U2, as reflected in the PROMETHE II ranking shown in Table 11. This indicates that economic feasibility and return on investment remain dominant considerations in strategic decision-making, even within the broader Industry 5.0 context. The finding aligns with common managerial practice, where resource allocation for new technologies and organizational change is primarily driven by profit-loss assessments. This indicates a potential disconnection between the aspirational dimensions of sustainability and human-centricity and the economic priorities of decision-makers. From a managerial perspective, this underlines the need to integrate financial rationale with innovation and sustainability to facilitate the practical adoption of future-oriented technologies. The comparison of rankings across the three perspectives U1, U2, and U3 provides a direct answer to research question Q3. The results clearly demonstrate that the adopted assessment perspective has a significant influence on the technology hierarchy. Sometechnologies are highly rated in technological terms and may have lower value in economic or organizational terms and vice versa. The results of this study are consistent with previous analyses on technology prioritization in the context of Industry 4.0 and 5.0. For example, the research conducted by [PERSON] and [PERSON] [87] showed that vertical integration is a key technology influencing success factors in Industry 4.0 implementation. A hybrid F-BWM and CoCoSo approach was applied to identify and evaluate these factors. In the context of small- and medium-sized enterprises (SMEs), [PERSON] [88] developed a performance evaluation model based on a hybrid SF-AHP-WSM approach, incorporating criteria such as software, manufacturing, and external partners. This model emphasizes the role of data integration and automation of information flows, which aligns with our findings regarding technologies L22-L23. Regarding AI applications in manufacturing, the literature review conducted by [PERSON] et al. [89] highlights the potential of Deep Reinforcement Learning (DRL) to improve the resilience and adaptability of manufacturing processes, outperforming traditional methods. This confirms the importance of AI-based technologies, such as L21-L22, identified as important in our study. The consistency of the results with the literature confirms the validity of the applied MCDA approach and its usefulness in the analysis of technology priorities and strategic planning of industrial transformation. Across industries, technologies related to AI, the IoT, data integration, and cybersecurity consistently emerge as core components of industrial development strategies of the industrial sector, reinforcing their central role in long-term competitiveness and innovation.