Open AccessANALISIS KEPUASAN DAN LOYALITAS PELANGGAN DALAM PEMESANAN TIKET PESAWAT SECARA ONLINE MENGGUNAKAN PENDEKATAN PARTIAL LEAST SQUARE (PLS)
Author(s) -
Trisnawati Gusnawita Berutu,
Abdul Hoyyi,
Sugito Sugito
Publication year - 2018
Publication title -
jurnal gaussian : jurnal statistika undip
Language(s) - English
Resource type - Journals
ISSN - 2339-2541
DOI - 10.14710/j.gauss.v7i4.28863
Subject(s) - latent variable , structural equation modeling , loyalty , customer satisfaction , service (business) , business , computer science , the internet , loyalty business model , variable (mathematics) , marketing , advertising , service quality , world wide web , mathematics , artificial intelligence , mathematical analysis , machine learning
Technology advances are bring rapid changes, thus bringing the world to the information society. From this technological progress thus e-commerce emerged, as a means to meet the needs of goods and services through internet access (online). This is what the airlines utilized by cooperating with various internet service providers (online), to provide convenience and comfort of airplane passengers in buying tickets without having to come directly to the place and through intermediaries. To provide the best service, need to know what factors that influence customer satisfaction in ordering airline tickets online. Appropriate modeling for this problem using structural equation modeling, with Partial Least Square (PLS) approach. The PLS approach is chosen because it is not based on several assumptions, one of these is the normal multivariate assumption. In this research, the exogenous latent variables used are performance, access, security, sensation, information, and web design, while the endogenous latent variables are satisfaction and loyalty. Based on the results of the analysis it can be concluded that the latent variables of access, security, sensation, information, and web design are able to explain the latent satisfaction variable of 70.32% while the satisfaction latent variable is able to explain the latent variable of loyalty by 36.02%.