TY - JOUR
T1 - Application of Support Vector Machines in Evaluating the Internationalization Success of Companies
AU - Rustam, Z.
AU - Yaurita, F.
AU - Segovia-Vergas, M. J.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - The internationalization started to be seen as an opportunity for many companies. This is one of the most crucial growth strategies for companies. Internationalization can be defined as a corporative strategy for growing through foreign markets. It can enhance the product lifetime and improve productivity and business efficiency. However, there is no general model for a successful international company. Therefore, the success of an internationalization procedure must be estimated based on different variables such as the status, strategy, and market characteristics of the company. In this paper, we try to build a model in evaluating the internationalization success of a company based on existing past data by using Support Vector Machines. The results are very encouraging and show that Support Vector Machines can be a useful tool in this sector. We found that Support Vector Machines achieved 81.36% accuracy rate with RBF Kernel, 80% training set, and σ = 0.05
AB - The internationalization started to be seen as an opportunity for many companies. This is one of the most crucial growth strategies for companies. Internationalization can be defined as a corporative strategy for growing through foreign markets. It can enhance the product lifetime and improve productivity and business efficiency. However, there is no general model for a successful international company. Therefore, the success of an internationalization procedure must be estimated based on different variables such as the status, strategy, and market characteristics of the company. In this paper, we try to build a model in evaluating the internationalization success of a company based on existing past data by using Support Vector Machines. The results are very encouraging and show that Support Vector Machines can be a useful tool in this sector. We found that Support Vector Machines achieved 81.36% accuracy rate with RBF Kernel, 80% training set, and σ = 0.05
UR - http://www.scopus.com/inward/record.url?scp=85058291737&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1108/1/012038
DO - 10.1088/1742-6596/1108/1/012038
M3 - Conference article
AN - SCOPUS:85058291737
SN - 1742-6588
VL - 1108
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012038
T2 - 2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018
Y2 - 21 July 2018
ER -