Digital transformation represents a compelling opportunity for manufacturing companies to enhance their competitiveness. This transformative journey offers myriad possibilities, including improved connectivity between workers and machines, as well as seamless machine-to-machine interactions. However, many manufacturing companies encounter challenges when attempting to implement digital transformation effectively. The process of digital transformation is often slow, and most companies find themselves in the early stages of adoption, grappling with the ambiguity surrounding the associated technologies. A systematic approach for the implementation of digital transformation is still elusive for many manufacturing companies. The number of studies exploring digital transformation is increasingly growing, encompassing various sectors and domains. However, within the manufacturing sector, there remains a need for further research and clarity on systematic implementation approaches. To address these issues, this research undertakes a comprehensive analysis to identify the critical factors that influence digital transformation in the manufacturing sector. The objective of this research is to identify the factors that drive the success of digital transformation in manufacturing companies while also uncovering factors that, when neglected, could lead to failure. Through a systematic literature review, this research identifies 11 critical factors. These factors serve as the basis for developing the ARTO model, a structured framework comprising four distinct categories: "Awareness-related factors," "Readiness-related factors," "Technology Selection-related factors," and "Operations-related factors." Moreover, this research incorporates expert perspectives gathered through a survey to refine the ARTO model. This study offers the ARTO model and digital transformation definition as practical tools for successfully implementing digital transformation in manufacturing companies, while also delineating the intricate relationships among the crucial factors. By shedding light on the factors underpinning digital transformation in the manufacturing sector, this research contributes to the ongoing discourse and facilitates more effective adoption of digital transformation strategies.

Purpose - This study proposes a distinct ecosystem orchestration concept, with the idea to address some of the technology and value proposition uncertainties that can occur during the birth phase of an innovation ecosystem linked to its actor's relation with each other as well as policymakers and customers. In order to better explain the proposed orchestration concept, Mobility as a Service (MaaS), a technological and social complex, innovative ecosystem, was chosen. Design/methodology/approach - The suggested touring orchestrating model emerges by utilizing multiple case study analyses, focusing on ecosystem construct, orchestration mechanism, and various actors of five selected mobility as service use cases, each presenting one category with unique characteristics. The analysis is complemented by a multi-vocal literature review (MLR) of secondary data. Findings - The study findings reveal substantial barriers to successful collaboration between innovation ecosystem actors, using traditional ways of orchestrating the ecosystem due to competition and unwillingness to invest for the benefit of others or risk of losing their customer base to competitors by joining a MaaS ecosystem, particularly when a new actor as orchestrator is onboarded. Additionally, there is a need to increase incentives and to enhance offerings in order to generate demand and attract stakeholders toward a new innovation ecosystem like MaaS. Research limitations/implications - Most of the models reviewed in this study are predominantly successful examples of mobility as a service originating from northern Europe and the Baltic region, potentially shaped by the characteristics of these markets. This regional focus represents a limitation of the study. Furthermore, the study's conceptual nature and lack of practical testing and empirical data support are additional limitations which could be addressed in future research through empirical investigation. The results of this study could assist in shaping future research and contribute to the development of more effective orchestration models and stakeholder management frameworks for managing innovation ecosystems across different industry contexts. Practical implications - The proposed touring orchestration model (TOM) provides insights not only for the actors in transportation industry but also for providers in other industries; on how to manage uncertainties and risks tied to technology and value proposition while advancing seamless cross-firm collaboration with other market actors during the formation of an innovation ecosystem. It can also facilitate the emergence of unexplored cross-industry business models by leveraging various data-sharing frameworks. The proposed model can also streamline processes and lower costs for policymakers in encouraging transportation and mobility providers to participate in MaaS, as it reduces risks and can offer greater financial advantages for these stakeholders. Originality/value - The findings of this study enhance the evolving ecosystem literature by exploring orchestration amid technological and value proposition uncertainties. Additionally, this study contributes to the expanding research on MaaS business models and ecosystem orchestration by leveraging data as a service-sharing model.