ArticleScientific reports2024
Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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0 citing papers in PubMed, 4 citations in OpenAlex.
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Authors and funding
5 authors at 2 institutions in 1 country.
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Abstract
Chinese media companies are facing opportunities and challenges brought about by digital transformation. Media economics takes the evaluation of the business results of media companies as the main research topic. However, overcoming the internal differences in the industry and comprehensively predicting the digital transformation of Chinese media companies from multiple dimensions has become an important issue to be understood. Based on the "TOE-I" theoretical framework, this study innovatively uses machine learning methods to predict the digital transformation of Chinese media companies and to analyze specific modes of the main driving factors affecting the digital transformation, using data from China's A-share-listed media companies from 2010 to 2020. The study found that environmental drivers can most effectively and accurately predict the digital transformation of Chinese media companies. Therefore, under sustained and stable economic and financial policies, guiding inter-industry competition and providing balanced digital infrastructure conditions are keys to bridging internal barriers in the media industry and promoting digital transformation. In the process of transformation from traditional content to digital production, media companies should focus on policy changes, economic benefits, the decision-making role of core managers, and the training and preservation of digital technology talent.
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