# Towards a Digital Transformation Hyper-Framework:
## 2 Research Scope and Hypothesis Formulation  
While engineering or re-engineering complex systems, the analysis, specification, and modeling activities naturally follow a top-down approach in specifying the global characteristic of a system under consideration as broadly as possible. On the contrary, design, implementation, verification, validation, deployment, and operational activities naturally follow a bottom-up approach that guarantees gradual and leaned transformation that does not jeopardize continuous service delivery. To correctly determine a research scope, we claim that the holistic approach, although the most challenging, guarantees the appropriate comprehension of the DgT's inherent complexity and enables the simultaneous formulation and application of a lifelong DgT methodology on macro, mezzo, and micro levels. This research activity is essential in creating the domain foundations that enable other researchers to build different solutions on top of mutually acceptable drivers. According to the general finding elaborated in the Introductory section, the most influencing DgT drivers are closely related to recent industrial revolution frameworks, starting from the three-dimensional Reference Architectural Model Industry 4.0 (RAMI 4.0) and its virtual description of production or service objects with Architecture (interoperability layers), Life Cycle, and Value Stream (differentiating product type and product instances), and Hierarchy (hosting of the production process) dimensions [10] (Figure 1). Industry 4.0 frames a digital transformation from traditional to cyber-physical systems (CPS) with economic benefits in mind. It utilizes innovative methods and technological means to upgrade the organizational system resources (organizational structure, business models, human resources, processes, and assets). Therefore, the existence of an intelligent enterprise information system aids the continuous rise of the system's maturity level. That  
Figure 1: Reference architectural model Industry 4.0—mind map diagram (adapted from [10]). is why developing an assessment tool for digital transformation progress tracking, aligned with the Industry 4.0 drivers, appears mandatory [11]. The following essential technology pillars appear as supportive of digital transformation towards Industry 4.0: Big Data and Analytics, Digital Twins and Simulation, Industrial Internet of Things, Augmented Reality, Autonomous Robots, Horizontal and Vertical Software Integration, Cybersecurity, and Cloud-based Additive Manufacturing [12]. While Industry 4.0 assumes intelligent manufacturing with variable degrees of strategic impacts on business model transformation, the importance of intelligent support in the decision-making process, based on the embedded fuzziness of key Industry 4.0 transformation success factors, emerges [13]. On the other hand, Industry 5.0 returns the essential human values and vital social needs on the DgT scene with the recognized duality of virtual and real-world systems. The transition from Industry 4.0 to Industry 5.0 exhibits a four-dimensional paradigm shift: from information to intelligence, from communication to connectivity, from cyber-physical to cyber-physical-social systems, and from physical automation to knowledge automation [14]. The essential novel technologies associated with the Industry 5.0 paradigm include Blockchain, Autonomous Area Vehicles, 5G and 6G Wireless Networks, Exoskeleton, Mixed Reality, Additive Manufacturing, Artificial Internet of Things, Motion Capture, and Digital Twin [15]. Further propositions concerning novel industry revolutions, like Industry 6.0, represent symbiotic integration of technological transformation, production chain innovation, and social sustainability progression towards the Smart Factory 6.0 paradigm as a sophisticated automated manufacturing facility that integrates emerging technologies and digital systems.