Analysis of the Influence Factors of College Students Employment Based on the Interpretative Structural Model
 
 
 
More details
Hide details
1
Hebei University of Science and Technology, CHINA
 
 
Online publication date: 2017-08-22
 
 
Publication date: 2017-08-22
 
 
Corresponding author
Zi-Yu Liu   

Assistant Professor, School of Economics and Management, Hebei University of Science and Technology, China. Address to No.26, YuXiang Rd., YuHuang Dist., Shijiazhuang City 050018, China (C.H.N.). Tel: +86-15203111616
 
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):5107-5114
 
KEYWORDS
ABSTRACT
Aimed at the problem of low employment rate of college students, we establish the evaluation index system of university students’ employment in this paper. Then we use the practical method of interpretative structural model to analyze the influence factors of university students’ employment, and explain the factors that affect college students’ employment model. According to the hierarchical results of interpretative structural model, we analyze the connection between the various factors, and divide the influence factors of university students’ employment into four levels. Finally, we clear the hierarchical structure relationship of factors that affect college students’ employment, and find out the most direct factors and the most fundamental factors which cause the low employment rate of college students.
 
REFERENCES (17)
1.
Borade, A. B., & Bansod, S. V. (2011). Interpretive structural modeling-based framework for VMI adoption in Indian industries. International Journal of Advanced Manufacturing Technology, 58(9-12), 1227-1242. doi:10.1007/s00170-011-3458-4.
 
2.
Chandramowli, S., Transue, M., & Felder, F. A. (2011). Analysis of barriers to development in landfill communities using interpretive structural modeling. Habitat International, 35(2), 246-253. doi:10.1016/j.habitatint.2010.09.005.
 
3.
Gao, Y. (2016). On gender difference in employment of the minority college students in northwest China. Journal of Northwest Normal University, 53(3), 118-123.
 
4.
Govindan, K., Palaniappan, M., & Zhu, Q. et al. (2012). Analysis of third party reverse logistics provider using interpretive structural modeling. Netherlands: Elsevier. doi:10.1016/j.ijpe.2012.01.043.
 
5.
Huang, J. (2010). Research on the influence of family background on the employment of College Students. Changsha: Hunan Normal University master’s thesis.
 
6.
Huang, W., Li, Z., & Li, Y. (2016). An empirical study on the influence factors of APP extension based on interpretative structural model. Knowledge Management Forum, 1(1), 61-73.
 
7.
Huang, W., Zhang, L., Lu, J., & Zhang, C. (2014). Analysis of the influence factors of the rammed earth wall quality based on interpretative structural model. Xi’an University of Architecture & Technology (Natural Science), 46(3), 333-341. doi:10.3969/j.issn.1006-7930.2014.03.006.
 
8.
Jiao, A., & Guo, X. (2016). Analysis of influencing factors of rural science and technology entrepreneurship based on interpretative structural model. Technology Management Research, 4, 212-217.
 
9.
Kempen, G. I., Ballemans, J., & Ranchor, A. V. et al. (2012). The impact of low vision on activities of daily living, symptoms of depression, feelings of anxiety and support in community-living older adults seeking vision rehabilitation services. Care Rehabilitat, 21(8), 1405-1411. doi:10.1007/s11136-011-0103-5.
 
10.
Liu, J. (2010). Exploration and practice of employment quality evaluation index system of graduates of Higher Vocational Colleges. China Electric Power Education, 21, 165-167.
 
11.
Li, Q. (2012). Research on evaluation index system of university graduates’ Employment Quality. Northeast Normal University.
 
12.
Li, P., Zhao, J., & Zeng, Y. (2011). The research summarize of the competitiveness of university graduates’ Employment. Inheritance, 32, 66-67. doi:10.3969/j.issn.1672-7894.2010.05.116.
 
13.
Ma, B., Peng, Y., & Guo, J. (2010). Fuzzy clustering analysis of the influence factors of college students employment in agriculture and forestry universities. Journal of Anhui Agriculture Science, 38(32), 18602, 18610. doi:10.13989/j.cnki.0517-6611.2010.32.195.
 
14.
Mathiyazhagan, K., Govindan, K., Noorul Haq, A., & Geng, Y. (2013). An ISM approach for the barrier analysis in implementing green supply chain management. Journal of Cleaner Production, 47, 283-297. doi:10.1016/j.jclepro.2012.10.042.
 
15.
Ren, D. (2010). Analysis of the influencing factors of College Students’ employment ability. Liaoning Technical University, 9, 555-557. doi:10.3969/j.issn.1008-391X.2010.05.032.
 
16.
Yang, X., & Chen, J. (2013). The explanation structure model of knowledge flow in Supply chain coordination. Soft Science, 27(5), 140-144. doi:10.3969/j.issn.1001-8409.2013.05.030.
 
17.
Zhu, Z., Wang, Q., & Zhang, S. (2014). Investigation and analysis of College Students’ science and Technology Entrepreneurship. Contemporary Educational Theory and Practice, 3, 50-52. doi:10.3969/j.issn.1674-5884.2014.03.019.
 
eISSN:1305-8223
ISSN:1305-8215
Journals System - logo
Scroll to top