User Acceptance of a Proposed Static-Dynamic Employment Recommendation Approach Among Computer Science Graduating Students

Employment recommendation services are increasingly used to support graduate job search. However, limited research has examined how graduating computer science students perceive a proposed employment recommendation approach that combines static profile-based matching with dynamic interactive functions. Drawing primarily on the Technology Acceptance Model (TAM), with selected dimensions of the Information System (IS) Success Model used as supplement, this study conducted an exploratory questionnaire-based survey of 386 graduating students. The respondents evaluated existing employment recommendation systems and provided open-ended comments, and the findings show that only 38.3% of respondents reported willingness to use existing employment recommendation systems for job hunting. The main reported problems were delayed matching to individual qualifications (71.0%), information lag (55.4%), and jobs not matching students’ majors (54.1%). In contrast, respondents expressed relatively favorable attitudes toward the proposed static-dynamic approach: 67.6% indicated willingness to use it and 59.6% indicated willingness to recommend it to others. Exploratory subgroup analyses further suggested that positive evaluations of the proposed approach were higher among students from emerging computing fields and those with more active job-seeking engagement (p < 0.05). Overall, the findings provide exploratory evidence that graduating computer science students may respond more positively to employment recommendation concepts that integrate profile-based matching with dynamic interaction. However, it is a proposed design concept, not an implemented system, evaluated by the respondents. Therefore, the results should be interpreted as perceptions and stated intentions, instead of evidence of actual adoption or real-world system effectiveness.