modeling and simulation
Atoosa Ebrahimi Shah Abadi; Jahangir Yadollahi Farsi; Niloofar Nobari
Abstract
The rapid growth of AI-based platforms in the past decade has made it necessary to examine the technological and managerial dimensions of this phenomenon. This research aimed to conduct a comparative analysis of the structure and consequences of three types of transactional, innovative, and cohesive ...
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The rapid growth of AI-based platforms in the past decade has made it necessary to examine the technological and managerial dimensions of this phenomenon. This research aimed to conduct a comparative analysis of the structure and consequences of three types of transactional, innovative, and cohesive platforms within the ADO framework. The present study was conducted using meta-synthesis and qualitative analysis, through three stages of open, axial, and selective coding, based on a review of 70 selected articles from 2015 to 2025, and the data were examined in three axes of prerequisites, decisions, and consequences. The analytical framework, combining platform categorization and the ADO model, provided the basis for a detailed comparison of key components. The results showed that transactional platforms with microservice architecture and open API, innovation platforms with deep learning frameworks and GPU/TPU, and cohesive platforms with data integrity and big data governance, took different paths to improve productivity and information security. The findings also highlighted that cloud computing, data governance, and information security were common and essential elements of these types. The analysis showed that the application of the ADO framework can help design effective policies for the management, security, and development of data-driven ecosystems and provide new insights for researchers and policymakers on how to optimize the performance of AI platforms.
