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Journal : Journal of Applied Data Sciences

Applying Factor Analysis to Assess Employment Competitiveness Strategies: A Data Science Perspective Wang, Yang; Sangsawang, Thosporn; Vipahasna, Piyanan Pannim; Vipahasna, Kitipoom; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.650

Abstract

This study aims to identify and analyze the factors influencing the employment competitiveness of graduates from higher vocational colleges in China and evaluate the impact of targeted programs designed to enhance these factors on graduates' employability. The research involved 17 experts and 100 instructors from Sichuan University of Science and Engineering, utilizing purposive sampling to explore effective career guidance models for improving employment ability. The Delphi technique was applied to synthesize expert opinions on key factors affecting graduate employment competitiveness. Additionally, a sample of undergraduate students participated in the study, with data collected through questionnaires. The findings demonstrate the transformative potential of focused career guidance programs, showing a significant improvement in students' employability post-intervention. These results emphasize the importance of targeted initiatives that equip students with the necessary skills, resources, and career insights to succeed in the job market. By bridging the gap between academia and industry expectations, such programs play a crucial role in preparing students for a smooth transition from university to the professional world, helping them secure meaningful employment opportunities.