Research On The Influence Of The Types Of Adviser On The Advice Taking In The Context Of Stock Price Prediction

In the era of digital economy,the rapid development of a new generation of information technology represented by artificial intelligence and big data has greatly promoted the digital transformation and upgrading of enterprises and effectively stimulated the new vitality of enterprises.With the development of the digital age,people not only face the massive growth of information,but also have more sources of suggestions.As one of the important themes in the field of decision-making,suggestion adoption has attracted the attention of more and more researchers.The influencing factors of recommendation adoption have been widely studied.Previous studies have shown that recommendation adoption will be affected by the characteristics of proposer,task,proposal and proposer.In recent years,the research on the adoption of suggestions by proponents with different characteristics has become increasingly rich,and the progress of artificial intelligence technology has provided decision makers with richer channels to obtain suggestions.In the prediction scenario of stock price,investors can use intelligent investment advisers to improve the quality of investment decision-making and better analyze the trend of stock price,but people's distrust of algorithms may affect this adoption tendency.Therefore,in order to better explore the decision-makers' suggestion adoption behavior of different suggestion sources in the current situation of information overload,this thesis selects man-machine suggestions as independent variables to explore the relationship between them and suggestion adoption in the context of stock price prediction,and further explore the regulatory role of task difficulty and cognitive closure needs,so as to improve the quality of suggestions and promote decision-makers to make efficient use of suggestions.Based on the above research contents,this study compiles the experimental program through E-Prime 2.0 software,designs three experimental tasks,and deeply discusses the influence of man-machine suggestions on the adoption of suggestions and the related interaction.Among them,experiment 1 explored the tendency of individuals to adopt manmachine suggestions in the context of stock price prediction;On this basis,experiment 2explored whether the adoption tendency would change when the task difficulty was different;In Experiment 3,the need for cognitive closure was introduced to explore the human-computer suggestion adoption tendency of individuals with different cognitive characteristics.The final results show that: in the stock price prediction situation,(1)man-machine suggestions have a significant impact on the adoption of suggestions,and individuals tend to adopt machine suggestions;(2)Human computer suggestion and task difficulty have significant interaction on suggestion adoption.In difficult tasks,decision makers are more inclined to adopt machine suggestions;In simple tasks,there is no significant difference in the impact of man-machine recommendations on the adoption of recommendations.(3)There is a significant difference between the recommendations with high cognitive needs and the recommendations with high cognitive needs for UAV interaction;For individuals with low cognitive closure needs,they are more inclined to adopt the suggestions of machines.