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Businesses of all kinds are breaching into Industry 5.0, and they require pursuing digital transformation (DT) with great aggression as it provides them the leverage in getting a competitive advantage, more numbers on top line as revenue, and much deeper relationships from a customer standpoint. DT is a far-reaching organizational change necessitating the integration of technologies such as blockchain, IoT, AI, and Cloud Computing. The article aims to emphasize the importance of theoretical frameworks to guide successful DT in organizations. It critiques the role of these frameworks in addressing common challenges seen in DT programs. The study reviews and analyses a range of theoretical frameworks such as Technology Acceptance Model (TAM), Task-Technology fit, and the Unified Theory of acceptance and use technology (UTAUT) extensively. This article reviews existing models and synthesizes their findings from thorough research in a wide range of organizational environments to understand how they can be applied and have been used within DT projects. The proposed article asserts that the main reason behind most of the failure of DT initiatives is planning which does not take into account many strategic factors and one of those factors are flexible organization and cultural integration. This concludes how the ever-ongoing problem of adopting technology is no longer unexplored by stating that TAM, TTF, and UTAUT can be successfully used to let organizations overcome these hurdles. The article underscores the need for strategic, culturally appropriate approaches to DT and highlights that theoretical underpinnings are integral in this effort. By embedding TAM, TTF, and UTAUT into a typical DT strategy, the way businesses navigate their complex environment in time is more likely to acknowledge where technological development should align with strategic goals as well as cultural values.

Background: The merging of computer science and robotic technology creates a synergistic environment where advances are changing healthcare, manufacturing, and service industries. Although some publications have shown their successes, complete research on the fusion and interface of computer science algorithms and robotic tasks is still needed. Objective: Combining computer science with robotics improves cooperation and integration. It also illustrates how linking these technologies might boost robotic entities' computational and operational capacities, making them more valuable and versatile. Methods: This mixed-methods research uses quantitative data from technical and computational performance metrics and qualitative insights from expert interviews and sector case studies. A critical study examines practical applications, issues of computer science and robotics. Results: Advanced computer algorithms may enhance robotic technologies by increasing autonomous decision-making, sensorimotor functions, and machine learning. However, the inquiry finds significant obstacles, notably in security, and implementation of these developments. Conclusion: The merger of computer science and robotic technology signals new possibilities and advancements, enabling the production of intelligent, adaptable, and autonomous robots. Combining these fields may improve robotic capabilities, but security, and practical application issues must be addressed to ensure sustainable and responsible technological growth.