Big data–enabled analysis of users’ behavioral intention in AI–based job search services

With the core functions of accurate job–candidate matching and scene–based intelligent simulation, AI–based job search applications are transforming the traditional human resources market and are becoming a vital technological solution for alleviating structural employment issues and promoting high–quality employment. However, AI–based job search is currently in its infancy, facing issues such as users’ cognitive bias and a lack of technical trust. To address these challenges, this study focuses on the popular topic of AI–based job search and proposes a multi–method fusion analysis framework. Firstly, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank and analyse the importance of the various factors influencing usage intention, clarifying the main factors. Secondly, factor analysis is employed to identify potential factors affecting users’ willingness to use AI–based job search services. Finally, partial least squares path modeling (PLS–PM) is employed to study the relationships between the potential factors and to explore how each factor specifically influences users’ willingness to use AI–based job search services. The results show that the top three important factors are data processing speed, the use of AI big models by people around them, and the accuracy of AI big model algorithms. The cumulative variance interpretation rate of the six extracted potential factors is 76.97%. Five influence paths were identified between the potential factors and willingness to use, indicating that the analysis framework constructed in this study can better explain users’ willingness to use AI–based job search services. This is highly significant in terms of improving the users’ experience of AI–based job search applications, optimizing product design, and promoting the application of AI technology in the field of job search.