User Acceptance Analysis of a Static-Dynamic Employment Recommendation System for Computer Science Graduates

Employment recommendation systems are increasingly used to support graduate job matching. However, limited research has examined how graduating computer science students perceive and respond to a proposed employment recommendation approach that combines static information matching with dynamic interactive functions. Drawing on the Technology Acceptance Model (TAM) and Information System (IS) Success Model, this study conducted a questionnaire-based survey of 386 graduating students and included an exploratory assessment of the questionnaire’s internal consistency and construct structure. The findings show that only 38.3% of respondents reported willingness to use existing employment recommendation systems for job hunting, citing critical limitations including delayed matching to individual qualifications (71.0%), information lag (55.4%), and jobs not matching majors (54.1%). In contrast, respondents reported more favorable attitudes toward the proposed static-dynamic job recommendation approach: 67.6% expressed willingness to use it and 59.6% expressed willingness to recommend it to others. Subgroup analyses reveal that students from emerging computing fields (e.g., AI, Data Science) and those in active job-seeking status demonstrated significantly higher perceived usefulness (PU) and behavioral intention (BI) (p < 0.05). These results underscore a significant “trust gap” in current platforms and suggest that future systems must transition from passive matching to dynamic, user-centric engagement. This research provides a practical blueprint for developing more responsive digital career services that address the evolving complexities of the computer science labor market.