Customer Loyalty in the Covid-19 Pandemic: The Application of Machine Learning in Survey Data

被引:8
|
作者
Bui Thanh Khoa [1 ]
Nguyen Thi Trang Oanh [1 ]
Vo Thi Thao Uyen [1 ]
Dang Cuu Hanh Dung [1 ]
机构
[1] Ind Univ Ho Chi Minh City, Ho Chi Minh City, Vietnam
关键词
SERVICE QUALITY; PERCEIVED RISK; ETHNOCENTRISM; SATISFACTION; TRUST;
D O I
10.1007/978-981-16-2877-1_38
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
E-commerce has brought many benefits to businesses and customers. However, the online market's fierce competition is also pressure for businesses, especially when the electronic marketplace (e-marketplace) is launched. The competition takes place in e-marketplace not only between brands but also between foreign and domestic businesses. This study is aimed to explore the effects of trust, customer ethnocentrism, service quality, and perceived risk on customer loyalty. The mixed-method research was applied to archive the research objective. Based on the machine learning computation, which became more popular and gained significant traction in the research world, the result showed that trust, ethnocentrism, and service quality positively impacted customer loyalty; perceived risk negatively affected customer loyalty toward domestic products on the e-marketplace in the Covid-19 pandemic. Some managerial implications were also proposed based on the research result.
引用
收藏
页码:419 / 429
页数:11
相关论文
共 50 条
  • [31] Entrepreneurship and self-service technologies as a driver of customer loyalty to the retailer during the COVID-19 pandemic
    Alves, Filipa Freitas
    Veloso, Cludia Miranda
    Felix, Elisabete Gomes Santana
    Sousa, Bruno Barbosa
    Valeri, Marco
    EUROMED JOURNAL OF BUSINESS, 2023,
  • [32] A review on use of data science for visualization and prediction of the covid-19 pandemic and early diagnosis of covid-19 using machine learning models
    Choubey S.K.
    Naman H.
    Studies in Big Data, 2020, 80 : 241 - 265
  • [33] Data Clustering Mapping of Covid-19 Pandemic Based On Geo-Location and Machine Learning
    Slaam, Mustafa Abdul
    Gouda, Karam
    Naguib, Ahmad
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2022, 22 (04): : 473 - 480
  • [34] Comprehensive Survey of Machine Learning Systems for COVID-19 Detection
    Alsaaidah, Bayan
    Al-Hadidi, Moh'd Rasoul
    Al-Nsour, Heba
    Masadeh, Raja
    AlZubi, Nael
    JOURNAL OF IMAGING, 2022, 8 (10)
  • [35] Destination loyalty and pandemic risks: Revisiting the study of tourist loyalty during the covid-19 pandemic
    Herrero-Crespo, Angel
    San Martin-Gutierrez, Hector
    Collado-Agudo, Jesus
    Garcia-de-los-Salmones-Sanchez, Maria-del-Mar
    TOURISM AND HOSPITALITY RESEARCH, 2024, 24 (02) : 241 - 256
  • [36] The economics of COVID-19 pandemic: A survey
    Padhan, Rakesh
    Prabheesh, K. P.
    ECONOMIC ANALYSIS AND POLICY, 2021, 70 : 220 - 237
  • [37] Analysis of COVID-19 pandemic and forecasting using machine learning models
    Chauhan, Ekansh
    Sirswal, Manpreet
    Gupta, Deepak
    Khanna, Ashish
    Khamparia, Aditya
    INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY, 2021, 66 (3-4) : 309 - 333
  • [38] Impact Assessment of COVID-19 Pandemic Through Machine Learning Models
    Alsolami, Fawaz Jaber
    Alghamdi, Abdullah Saad Al-Malaise
    Khan, Asif Irshad
    Abushark, Yoosef B.
    Almalawi, Abdulmohsen
    Saleem, Farrukh
    Agrawal, Alka
    Kumar, Rajeev
    Khan, Raees Ahmad
    CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 68 (03): : 2895 - 2912
  • [39] The COVID-19 pandemic: prediction study based on machine learning models
    Zohair Malki
    El-Sayed Atlam
    Ashraf Ewis
    Guesh Dagnew
    Osama A. Ghoneim
    Abdallah A. Mohamed
    Mohamed M. Abdel-Daim
    Ibrahim Gad
    Environmental Science and Pollution Research, 2021, 28 : 40496 - 40506
  • [40] COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers
    Sultana, Jabeen
    Singha, Anjani Kumar
    Siddiqui, Shams Tabrez
    Nagalaxmi, Guthikonda
    Sriram, Anil Kumar
    Pathak, Nitish
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2022, 32 (02): : 1007 - 1024