Unveiling the Mechanics of AI Adoption in Journalism: A Multi-Factorial Exploration of Expectation Confirmation, Knowledge Management, and Sustainable Use
Year: 2025
Type: article
Study design: Observational Study
Abstract: This study explores the factors influencing the sustainable adoption of AI technologies in journalism. It integrates the expectation confirmation model (ECM)—including expectation confirmation, perceived usefulness, and satisfaction—with knowledge management (KM) features (knowledge sharing, acquisition, and application), and other factors like ease of use, trust, and technological affinity. Data from an online survey of 396 Chinese journalists using AI for journalistic tasks were analyzed through structural equation modeling. Results show that expectation confirmation significantly influences perceived usefulness, while satisfaction is correlated with expectation confirmation, usefulness, and ease of use. Sustainable use of AI is impacted by usefulness, satisfaction, knowledge management practices, ease of use, trust, and technological affinity, with perceived usefulness being the most significant factor. These findings provide deeper insights into AI adoption in journalism, offering implications for AI developers, media enterprises, and training programs aimed at fostering sustainable AI use in journalism.
Source: Journalism and Media
Authors: Fangni Li, Hongyu Wang
Institution: Communication University of China
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W4410028188