The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach

被引:289
|
作者
Xu, Xun [1 ]
Li, Yibai [2 ]
机构
[1] Calif State Univ Stanislaus, Coll Business Adm, Dept Management Operat & Mkt, One Univ Circle, Turlock, CA 95382 USA
[2] Univ Scranton, Kania Sch Management, Operat & Informat Management Dept, Scranton, PA 18510 USA
关键词
Customer satisfaction; Customer dissatisfaction; Antecedents; Hotel type; Online reviews; Text mining; LATENT SEMANTIC ANALYSIS; ONLINE PRODUCT REVIEWS; USER-GENERATED CONTENT; WORD-OF-MOUTH; BEHAVIORAL INTENTIONS; GUEST SATISFACTION; SERVICE QUALITY; BUDGET HOTELS; HOSPITALITY; MANAGEMENT;
D O I
10.1016/j.ijhm.2016.03.003
中图分类号
F [经济];
学科分类号
02 ;
摘要
Customers' online reviews play an important role in generating electronic word of mouth; these reviews serve as an online communication tool that highly influences consumers' demand for hotels. Using latent semantic analysis, which is a text mining approach, we analyze online customer reviews of hotels. We find that the determinants that create either customer satisfaction or dissatisfaction toward hotels are different and are specific to particular types of hotels, including full-service hotels, limited-service hotels, suite hotels with food and beverage, and suite hotels without food and beverage. Our study provides a clue for hoteliers to enhance customer satisfaction and alleviate customer dissatisfaction by improving service and satisfying the customers' needs for the different types of hotels the hoteliers own. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:57 / 69
页数:13
相关论文
共 50 条
  • [31] Research on the Influential Factors of Customer Satisfaction for Hotels: The Artificial Neural Network Approach and Logistic Regression Analysis
    Huang, Han-Chen
    PROCEEDINGS OF THE 2012 INTERNATIONAL CONFERENCE OF MODERN COMPUTER SCIENCE AND APPLICATIONS, 2013, 191 : 441 - 448
  • [32] The Forecasting Sales Volume and Satisfaction of Organic Products through Text Mining on Web Customer Reviews
    Lyu, Fang
    Choi, Jaewon
    SUSTAINABILITY, 2020, 12 (11)
  • [33] Can customer sentiment impact firm value? An integrated text mining approach
    Eachempati, Prajwal
    Srivastava, Praveen Ranjan
    Kumar, Ajay
    Munoz de Prat, Javier
    Delen, Dursun
    TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2022, 174
  • [34] CUSTOMER PRODUCT EXPERIENCE ANALYSIS USING TEXT MINING: A NEURO LINGUISTIC PROGRAMMING APPROACH
    Mangaonkar, Nikhita
    Sirsat, Sudarshan
    2017 INTERNATIONAL CONFERENCE ON COMPUTING METHODOLOGIES AND COMMUNICATION (ICCMC), 2017, : 216 - 219
  • [35] Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach
    Ordenes, Francisco Villarroel
    Theodoulidis, Babis
    Burton, Jamie
    Gruber, Thorsten
    Zaki, Mohamed
    JOURNAL OF SERVICE RESEARCH, 2014, 17 (03) : 278 - 295
  • [36] Exploring antecedents impacting user satisfaction with voice assistant app: A text mining-based analysis on Alexa services
    Kumar, Anand
    Bala, Pradip Kumar
    Chakraborty, Shibashish
    Behera, Rajat Kumar
    JOURNAL OF RETAILING AND CONSUMER SERVICES, 2024, 76
  • [37] RESEARCH ON E-COMMERCE CUSTOMER SATISFACTION EVALUATION METHOD BASED ON PSO-LSTM AND TEXT MINING
    Yang, Qin
    3C EMPRESA, 2023, 12 (01): : 51 - 66
  • [38] Factors influencing customer satisfaction with AR shopping assistant applications in e-commerce: an empirical analysis utilizing text-mining techniques
    Ho, Jae-Yun
    Ju, Gyeong
    Hong, Seoeui
    An, Jaeyoung
    Lee, Choong C.
    ASLIB JOURNAL OF INFORMATION MANAGEMENT, 2025, 77 (02) : 239 - 259
  • [39] Understanding Antecedents That Affect Customer Evaluations of Head-Mounted Display VR Devices through Text Mining and Deep Neural Network
    Maeng, Yunho
    Lee, Choong C.
    Yun, Haejung
    JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, 2023, 18 (03): : 1238 - 1256
  • [40] Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules
    Wang, Tianxiong
    Zhou, Meiyu
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (01) : 331 - 353