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Top1. Introduction
In recent years, digital transformation (Ross et al., 2019) has significantly disrupted existing enterprises and economies. The Internet and related digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), data analytics, cloud computing, mobile IT systems, social collaboration networks, and cyber-physical systems are potential strategic drivers and enablers of digital platforms with fast-evolving ecosystems of intelligent systems and services (Zimmermann, Masuda et al., 2020). The IoT also provides a platform where objects and devices can be connected to the Internet to enable machine-to-machine (M2M) communication and the transfer of data using standard network protocols such as TCP/IP. The IoT combined with other technologies such as AI, machine learning, deep learning, and cloud computing, can change the face of robotics by introducing the next generation of intelligent robots (Batth et al., 2018). Advances in AI have dramatically accelerated in recent times, and they can solve specific tasks. For instance, AI algorithms can optimize inventory tasks, predict customer movements, predict potential device failure, and detect fraud on digital platforms (Siebel, 2019). Thus, business structures and process efficiencies can be enhanced using digital platforms such as portals and social networking services (SNSs) among corporations in many industries. We expect the use of these digital platforms for various purposes in industries at the forefront of the adoption of digital IT technologies.
Several global corporations have experienced various changes resulting from the emergence of new technologies, globalization, shifts in customer needs, and the implementation of new business models. Significant changes in cutting-edge IT technologies owing to recent developments in cloud computing and mobile IT, such as advances in big data technology, have emerged as new IT trends. Furthermore, major advances in the abovementioned technologies and processes have led to the creation of a “digital IT economy,” creating business opportunities and risks and forcing enterprises to innovate or face the consequences (Boardman & KPN, 2015). Enterprise systems (ESs) are complex application software packages that have mechanisms capable of supporting the management of the entire enterprise and integrating all areas of its operation (Davenport, 1998, p. 121). Although enterprise architecture (EA) is effective because it contributes to the design of large integrated systems, which face a major technical challenge toward the era of cloud/mobile IT/digital IT. From a comprehensive perspective, EA encompasses all enterprise artifacts, including business, organization, applications, data, and infrastructure, necessary to establish a current architecture and a future architecture/roadmap. Thus, EA frameworks need to accept changes in ways that adequately consider new emerging paradigms and requirements that affect EA, such as enterprise mobile IT/cloud computing (Buckl et al., 2010; Alwadain et al., 2014). However, the TOGAF is criticized for its size, lack of agility, and complexity (Gill et al., 2014). Hence, existing EA frameworks are not appropriate for digital transformation (Masuda et al., 2016).
Considering the above background, we proposed an adaptive integrated EA framework in our previous study, which aligns with an IT strategy that promotes cloud, mobile IT, and digital IT, and verified the framework using a case study (Masuda et al., 2017). The author of this paper named the EA framework suitable for the era of digital IT as the “Adaptive Integrated Digital Architecture Framework (AIDAF)” (Masuda et al., 2018). We expect that the above digital transformation and digital platforms for enterprises, as well as information societies with ecosystems, can be managed using AIDAF because digital platforms involve big data and cloud-related aspects.
This paper is organized as follows: In the following section, the research background is provided, followed by the proposed process and framework with the architecture board in the AIDAF and a description of the research methodology. Furthermore, the evaluations and analysis in the case studies are presented while the proposed process and framework are verified. Subsequently, the following processes with ecosystem platforms and directions are described. Finally, the limitations of the current research and directions for future research are outlined.