CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

How Individuals Influence Supply Chain Management Performance?

by Chen-Huei Chou
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 13
Year of Publication: 2020
Authors: Chen-Huei Chou
10.5120/ijca2020920084

Chen-Huei Chou . How Individuals Influence Supply Chain Management Performance?. International Journal of Computer Applications. 176, 13 ( Apr 2020), 35-39. DOI=10.5120/ijca2020920084

@article{ 10.5120/ijca2020920084,
author = { Chen-Huei Chou },
title = { How Individuals Influence Supply Chain Management Performance? },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2020 },
volume = { 176 },
number = { 13 },
month = { Apr },
year = { 2020 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number13/31263-2020920084/ },
doi = { 10.5120/ijca2020920084 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:26.340202+05:30
%A Chen-Huei Chou
%T How Individuals Influence Supply Chain Management Performance?
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 13
%P 35-39
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Supply chain management has been an important field in business operations. Due to the popularity of electronic commerce and mobile commerce, the supply chain field has been evolved to another level. Higher level of automation and use of computerized software have been deployed. Human intervention still cannot be avoided. Rather, human interactions play an important role streamlining the supply chain processes. Both individual and group human performances thus draw much attentions in the success of supply chain applications. However, little is known about the individual’s contribution to the performance in the field. This study aims to understand the individuals’ performance based on their individual personality traits. Attribute selection methods are used to identify the key personality traits in the Big Five Model.

References
  1. Abu-Salih, B., Wongthongtham, P. and Chan, K.Y., 2018. Twitter mining for ontology-based domain discovery incorporating machine learning. Journal of Knowledge Management.
  2. Avrim, L.B., and Pat, L. 1997. Selection of Relevant Features and Examples in Machine Learning., Artificial Intelligence (97:1-2) 1997, pp. 245-271.
  3. Cattell, R.B., 1943. The description of personality: Basic traits resolved into clusters. The journal of abnormal and social psychology, 38(4), pp. 476-506.
  4. Costa, P.T. and McCrae, R.R., 1985. The NEO personality inventory. Odessa, FL: Psychological Assessment Resources.
  5. Costa Jr, P.T. and McCrae, R.R., 2008. The Revised NEO Personality Inventory (NEO-PI-R). Sage Publications, Inc.
  6. Debole, F. and Sebastiani, F., 2004. Supervised term weighting for automated text categorization. In Text mining and its applications (pp. 81-97). Springer, Berlin, Heidelberg.
  7. Dougherty, T.W., Turban, D.B. and Callender, J.C., 1994. Confirming first impressions in the employment interview: A field study of interviewer behavior. Journal of applied psychology, 79(5), pp. 659-663.
  8. Forman, G., 2003. An extensive empirical study of feature selection metrics for text classification. Journal of machine learning research, 3(Mar), pp.1289-1305.
  9. Hall, M.A. and Holmes, G., 2003. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data engineering, 15(6), pp.1437-1447.
  10. Hogan, R.T., 1991. Personality and personality measurement: handbook of industrial and organizational psychology (Editor: MD Dunnette ve LM Hough). Palo Alto.
  11. Hogan R.T. Roberts B.W. 2001. Introduction: Personality and industrial and organizational psychology. In Roberts BW, Hogan R (Eds.), Personality psychology in the workplace (pp. 3-16). Washington, DC American Psychological Association.
  12. Kohavi, R. and John, G.H., 1997. Wrappers for feature subset selection. Artificial intelligence, 97(1-2), pp. 273-324.
  13. Liu, H. and Setiono, R., 1995, November. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence (pp. 388-391). IEEE.
  14. Maini, T., Kumar, A., Misra, R. and Singh, D., 2017, October. Rough set based feature selection using swarm intelligence with distributed sampled initialisation. In 2017 6th International Conference on Computer Applications In Electrical Engineering-Recent Advances (CERA) (pp. 92-97). IEEE.
  15. Osborne, R.E., 1998. Personality traits. Choice, 36, p. 223.
  16. Quinlan, J.R. C4.5, 1993. Programs for machine learning Morgan Kaufmann Publishers, San Francisco, USA.
  17. Saucier, G., 1994. Mini-Markers: A brief version of Goldberg's unipolar Big-Five markers. Journal of personality assessment, 63(3), pp. 506-516.
  18. Schminke, M. and Wells, D., 1999. Group processes and performance and their effects on individuals' ethical frameworks. Journal of Business Ethics, 18(4), pp.367-381.
  19. Sebastiani, F., 2002. Machine learning in automated text categorization. ACM computing surveys (CSUR), 34(1), pp.1-47.
  20. Witten, I.H., and Frank, E., 2016. Data mining: practical machine learning tools and techniques, fourth edition Morgan Kaufmann Publishers.
  21. Yang, Y. and Pedersen, J.O., 1997. Proceedings of ICML-97, 14th International Conference on Machine Learning, pp. 412- 420.
Index Terms

Computer Science
Information Sciences

Keywords

Personality Traits Attribute Selection Filter Wrapper Machine Learning

Powered by PhDFocusTM