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A Hybrid Method with TOPSIS and Machine Learning Techniques for Sustainable Development of Green Hotels Considering Online Reviews

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  • Mehrbakhsh Nilashi

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam)

  • Abbas Mardani

    (Department of Marketing, College of Business Administration, University of South Florida, Tampa, FL 33813, USA)

  • Huchang Liao

    (Business School, Sichuan University, Chengdu 610064, China
    Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain)

  • Hossein Ahmadi

    (Department of Information Technology, University of Human Development, Sulaymaniyah 00964, Iraq)

  • Azizah Abdul Manaf

    (Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia)

  • Wafa Almukadi

    (Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia)

Abstract

This paper proposes a hybrid method for online reviews analysis through multi-criteria decision-making, text mining and predictive learning techniques to find the relative importance of factors affecting travelers’ decision-making in selecting green hotels with spa services. The proposed method is developed for the first time in the context of tourism and hospitality by this research, especially for customer segmentation in green hotels through customers’ online reviews. We use Self-Organizing Map (SOM) for cluster analysis, Latent Dirichlet Analysis (LDA) technique for analyzing textual reviews, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking hotel features, and Neuro-Fuzzy technique to reveal the customer satisfaction levels. The impact of green hotels with spa and non-spa services on travelers’ satisfaction is investigated for four travelling groups: Travelled solo, Travelled with family, Travelled as a couple and Travelled with friends. The proposed method is evaluated on the travelers’ reviews on 152 hotels in Malaysia. The findings of this study provide an important method for travelers’ decision-making for hotel selection through User-Generated Content (UGC) and help hotel managers to improve their service quality and marketing strategies.

Suggested Citation

  • Mehrbakhsh Nilashi & Abbas Mardani & Huchang Liao & Hossein Ahmadi & Azizah Abdul Manaf & Wafa Almukadi, 2019. "A Hybrid Method with TOPSIS and Machine Learning Techniques for Sustainable Development of Green Hotels Considering Online Reviews," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6013-:d:281478
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    References listed on IDEAS

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    Cited by:

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    3. Juan Carlos Martín & Veronika Rudchenko & María-Victoria Sánchez-Rebull, 2020. "The Role of Nationality and Hotel Class on Guests’ Satisfaction. A Fuzzy-TOPSIS Approach Applied in Saint Petersburg," Administrative Sciences, MDPI, vol. 10(3), pages 1-24, September.
    4. Usman Ali & Huseyin Arasli & Furkan Arasli & Mehmet Bahri Saydam & Emel Capkiner & Emel Aksoy & Guzide Atai, 2023. "Determinants and Impacts of Quality Attributes on Guest Perceptions in Norwegian Green Hotels," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    5. Liu, Chih-Hsing & Horng, Jeou-Shyan & Chou, Sheng-Fang & Yu, Tai-Yi & Lee, Ming-Tsung & Lapuz, Maria Carmen B., 2023. "Discovery sustainable servicescape on behavioural intention practices and nationality: The moderating role of parasocial interaction," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    6. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    7. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    8. Nilashi, Mehrbakhsh & Ahmadi, Hossein & Arji, Goli & Alsalem, Khalaf Okab & Samad, Sarminah & Ghabban, Fahad & Alzahrani, Ahmed Omar & Ahani, Ali & Alarood, Ala Abdulsalam, 2021. "Big social data and customer decision making in vegetarian restaurants: A combined machine learning method," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    9. Na Zhang & Ping Yu & Yupeng Li & Wei Gao, 2022. "Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics," Sustainability, MDPI, vol. 14(24), pages 1-26, December.

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