Would you read a news article that was written by a bot? Source and message credibility in automated news in relation to readers’ choice of digital financial news. A quantitative research

Automated news is arguably one of the most contentious technological disruptions in the field of journalistic publication. The reasoning behind this is that conventional method of news indicated a systemic dependence on human resources with intermittent interference of machinery in the publishing process. Automation in news publication, financial or other, is an penetration of machinery, digitization and artificial intelligence into a literature-heavy world. Automation in news publication can potentially be developed and implemented cross-news-column, meaning financial news is merely the beginning of such disruption. This has long sparked heated discussion among professions in news reporting and journalism due to its existential threat of stealing jobs and replacing human in the news workforce. Nevertheless, research on automated news and its implication have not been as diverse in scope of impact, cause and future implications as it should. Financial news is one of the must-have columns of any business and economics newspaper. Its influence is more tangible than other columns in a newspaper due to its prevalence in the field of financial instrument trading and investment. As a result, the requirement of precision and creditworthiness for financial news reporting is upheld higher than other columns in the spectrum such as real estate and energy. This study was an attempt to contribute to the limited literature, regarding the customer perception of automated news by answering questions such as how automated financial news are perceived by financial news readers. As a result, to answer the question how financial news readers feel about automated news, the main research question is “Do message and source credibility of automated news affect finance news readers’ choice of news readership?” The sentiment was based on customer perception of source and message of automated news to determine which element decides the level of trustworthiness. The selected research method was quantitative which was conducted in a form of a survey that was created on Qualtrics. The survey was then distributed via personal connections and LinkedIn direct messages. The data from questionnaire was analyzed with SPSS to gain descriptive analytics on five elements, namely accuracy, completion, believability, trust, bias, all of which contribute to news credibility. The main finding of the study is that audience perceive financial news, both automated and human-written, in certain degree of credibility. The most surprising findings was that messages found in automated news content was deemed more accurate than human-written news content. Another notable findings was that the source of automated news content was perceived to achieve more reader satisfaction than human-written news content. The study also found that, because of the lack of sources in online journalism’s publication, financial news readers tend to rely on news medium and message cues found along a news article to decide where the information was credible or not. Researchers need to investigate automated news content and the disruption of automation in both creation and publication stages to further reflect readers’ attitude towards automation in news content. Journalists and news companies should determine which elements to be optimized using artificial intelligence and which type of news reporting content should be prioritized using the existing human journalist resources.