Investigating the determinants of homestay satisfaction on Airbnb using multiple techniques.
Airbnb
Homestay satisfaction
Random Forest
Sentiment analysis
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
15 10 2024
15 10 2024
Historique:
received:
30
04
2024
accepted:
08
10
2024
medline:
16
10
2024
pubmed:
16
10
2024
entrez:
15
10
2024
Statut:
epublish
Résumé
Peer-to-peer accommodation has gained prominence in the sharing economy and e-commerce sectors, with big data playing a crucial role in understanding customer preferences and evaluating homestay satisfaction. This study proposes a novel methodology that integrates Natural Language Processing (NLP) techniques, a Random Forest model, and Geographic Information System (GIS) functionalities to quantify the complex relationship between homestay satisfaction and diverse customer preferences. Notably, this study addresses the positive bias inherent in listing scores by segmenting homestays into three categories (satisfactory, moderate, and dissatisfactory) based on sentiment analysis from online reviews. Furthermore, this study not only identifies eight key determinants of homestay satisfaction but also unveils the nonlinear relationships and interactions between them. More significantly, we identify specific threshold values for geographic determinants, offering actionable recommendations for homestay planning and layout. These findings provide valuable insights that can be leveraged to improve homestay experiences and promote the sustainable development of urban homestays.
Identifiants
pubmed: 39406838
doi: 10.1038/s41598-024-75701-w
pii: 10.1038/s41598-024-75701-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24146Subventions
Organisme : Shenyang Philosophy Social Science planning project
ID : SY202210Q
Organisme : Liaoning Federation of Social Science project
ID : 20221slqnrcwtkt-50
Informations de copyright
© 2024. The Author(s).
Références
Lutz, C. & Newlands, G. Consumer segmentation within the sharing economy: the case of Airbnb. J. Bus. Res. 88, 187–196 (2018).
doi: 10.1016/j.jbusres.2018.03.019
Einav, L., Farronato, C. & Levin, J. Peer-to-peer markets. Annu. Rev. Econ. 8, 615–635 (2016).
doi: 10.1146/annurev-economics-080315-015334
Lee, S. & Kim, D. Y. Brand personality of Airbnb: application of user involvement and gender differences. J. Travel Tour Mark. 35, 32–45 (2018).
doi: 10.1080/10548408.2017.1284030
Airbnb About us. https://news.airbnb.com/zh/about-us/
Tandon, U., Kiran, R. & Sah, A. Analyzing customer satisfaction: users’ perspective towards online shopping. Nankai Bus. Rev. Int. 8, 266–288 (2017).
doi: 10.1108/NBRI-04-2016-0012
Bowling, A. Mode of questionnaire administration can have serious effects on data quality. J. Public Health. 27, 281–291 (2005).
doi: 10.1093/pubmed/fdi031
Albastroiu, I. & Felea, M. The implications of user-generated content websites for Tourism Marketing. J. Econ. Pract. Theor. 4, 222–229 (2014).
Dhar, V. & Chang, E. A. Does Chatter Matter? The impact of user-generated content on music sales. J. Interact. Mark. 23, 300–307 (2009).
doi: 10.1016/j.intmar.2009.07.004
Jiménez, F. R. & Mendoza, N. A. Too popular to ignore: the influence of Online Reviews on Purchase intentions of Search and Experience products. J. Interact. Mark. 27, 226–235 (2013).
doi: 10.1016/j.intmar.2013.04.004
Lee, M., Jeong, M. & Lee, J. Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website: a text mining approach. Int. J. Contemp. Hosp. Manag. 29, 762–783 (2017).
doi: 10.1108/IJCHM-10-2015-0626
Geetha, M., Singha, P. & Sinha, S. Relationship between customer sentiment and online customer ratings for hotels - an empirical analysis. Tour Manag. 61, 43–54 (2017).
doi: 10.1016/j.tourman.2016.12.022
Malik, M. S. I. & Hussain, A. Helpfulness of product reviews as a function of discrete positive and negative emotions. Comput. Hum. Behav. 73, 290–302 (2017).
doi: 10.1016/j.chb.2017.03.053
Mahr, D., Stead, S. & Odekerken-Schröder, G. Making sense of customer service experiences: a text mining review. J. Serv. Mark. 33, 88–103 (2019).
doi: 10.1108/JSM-10-2018-0295
Lancichinetti, A., Sirer, M. I. & Wang, J. X. High-reproducibility and high-accuracy method for automated topic classification. Phys. Rev. X. 5 (1), 011007 (2015).
Kotlerman, L., Dagan, I. & Szpektor, I. Directional distributional similarity for lexical expansion. in ACL-IJCNLP 2009 Conference Short Papers Association for Computational Linguistics, Suntec, Singapore, 69–72 (2009).
Lawani, A., Reed, M. R., Mark, T. & Zheng, Y. Reviews and price on online platforms: evidence from sentiment analysis of Airbnb reviews in Boston. Reg. Sci. Urban Econ. 75, 22–34 (2019).
doi: 10.1016/j.regsciurbeco.2018.11.003
Fradkin, A., Grewal, E., Holtz, D. & Pearson, M. Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb. in Proceedings of the Sixteenth ACM Conference on Economics and Computation ACM, Portland Oregon USA, 641 (2015).
Yannopoulou, N. User-Generated brands and Social Media: Couchsurfing and Airbnb. Contemp. Manag. Res. 9, 85–90 (2013).
doi: 10.7903/cmr.11116
Zekanovic-Korona, L. & Grzunov, J. Evaluation of shared digital economy adoption: case of Airbnb. in 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) 1574–1579, IEEE, Opatija, Croatia, (2014).
doi: 10.1109/MIPRO.2014.6859816
Bridges, J. & Vásquez, C. If nearly all Airbnb reviews are positive, does that make them meaningless? Curr. Issues Tour. 21, 2065–2083 (2018).
doi: 10.1080/13683500.2016.1267113
Ozyurt, B. & Akcayol, M. A. A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA. Expert Syst. Appl. 168, 114231 (2021).
doi: 10.1016/j.eswa.2020.114231
Rajaratnam, S. D., Nair, V., Pahlevan Sharif, S. & Munikrishnan, U. T. Destination quality and tourists’ behavioural intentions: rural tourist destinations in Malaysia. Worldw. Hosp. Tour Themes. 7, 463–472 (2015).
Young, C. A., Corsun, D. L. & Xie, K. L. Travelers’ preferences for peer-to-peer (P2P) accommodations and hotels. Int. J. Cult. Tour Hosp. 11 (4), 465–482 (2017).
doi: 10.1108/IJCTHR-09-2016-0093
Xu, B., Huang, J. Z., Williams, G., Wang, Q. & Ye, Y. Classifying very high-dimensional data with Random forests built from small subspaces. Int. J. Data Warehous. Min. 8, 44–63 (2012).
doi: 10.4018/jdwm.2012040103
Zhou, T., Cheng, X. & Development Status Problems and Suggestion of Beijing B&B Industry. in Annual Report On Homestay in China (2020–2021) (eds. Guo, J. & Wang, C.) 29–40 (Social Sciences Academic Press (CHINA), Beijing, China, (2021).
Wong, I. A. & Dioko, L. D. A. Understanding the mediated moderating role of customer expectations in the customer satisfaction model: the case of casinos. Tour. Manag. 36, 188–199 (2013).
doi: 10.1016/j.tourman.2012.10.010
Zhou, L., Ye, S., Pearce, P. L. & Wu, M. Y. Refreshing hotel satisfaction studies by reconfiguring customer review data. Int. J. Hosp. Manag. 38, 1–10 (2014).
doi: 10.1016/j.ijhm.2013.12.004
Kang, D. & Park, Y. Based measurement of customer satisfaction in mobile service: sentiment analysis and VIKOR approach. Expert Syst. Appl. 41, 1041–1050 (2014).
doi: 10.1016/j.eswa.2013.07.101
Alaei, A. R., Becken, S. & Stantic, B. Sentiment analysis in tourism: capitalizing on big data. J. Travel Res. 58, 175–191 (2019).
doi: 10.1177/0047287517747753
Cheng, M. & Jin, X. What do Airbnb users care about? An analysis of online review comments. Int. J. Hosp. Manag. 76, 58–70 (2019).
doi: 10.1016/j.ijhm.2018.04.004
Madhi, H. A. B. & Alhammah, M. M. What drives Airbnb customers’ satisfaction in Amsterdam? A sentiment analysis. Int. J. Adv. Comput. Sci. Appl. 12, 255–263 (2021).
Sánchez-Franco, M. J., Navarro-García, A. & Rondán-Cataluña, F. J. A naive Bayes strategy for classifying customer satisfaction: a study based on online reviews of hospitality services. J. Bus. Res. 101, 499–506 (2019).
doi: 10.1016/j.jbusres.2018.12.051
Kim, Y. J. & Kim, H. S. The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability. 14, 848 (2022). (2022).
Öğüt, H. & Onur Taş, B. K. The influence of internet customer reviews on the online sales and prices in hotel industry. SERV. Ind. J. 32, 197–214 (2012).
doi: 10.1080/02642069.2010.529436
Guttentag, D. Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector. Curr. Issues Tour. 18, 1192–1217 (2013).
doi: 10.1080/13683500.2013.827159
La, L., Xu, F. & Hu, M. Location of Airbnb and hotels: the spatial distribution and relationships. Tour Rev. 77, 209–224 (2022).
doi: 10.1108/TR-10-2020-0476
Ismail, M. N. I., Hanafiah, M. H., Aminuddin, N. & Mustafa, N. Community-based homestay service quality, visitor satisfaction, and behavioral intention. Procedia Soc. Behav. Sci. 222, 398–405 (2016). (2016).
Ju, Y., Back, K. J. & Choi, Y. Exploring Airbnb service quality attributes and their asymmetric effects on customer satisfaction. Int. J. Hosp. Manag. 77, 342–352 (2019).
doi: 10.1016/j.ijhm.2018.07.014
Tavana, M., Mousavi, S. M. H. & Mina, H. A dynamic decision support system for evaluating peer-to-peer rental accommodations in the sharing economy. Int. J. Hosp. Manag. 91, 102653 (2020).
doi: 10.1016/j.ijhm.2020.102653
Hong, I. & Yoo, C. Analyzing spatial variance of Airbnb pricing determinants using multiscale GWR approach. Sustainability. 12, 4710 (2020).
doi: 10.3390/su12114710
Xu, F., Hu, M. & La, L. The influence of neighbourhood environment on Airbnb: a geographically weighed regression analysis. Tour. Geogr. 3, 192–209 (2020).
doi: 10.1080/14616688.2019.1586987
Basu, S., Kumbier, K., Brown, J. B. & Yu, B. Iterative random forests to discover predictive and stable high-order interactions. PNAS. 115, 1943–1948 (2018).
pubmed: 29351989
pmcid: 5828575
doi: 10.1073/pnas.1711236115
Hussen Maulud, D., Zeebaree, S. R. M., Jacksi, K. & Mohammed Sadeeq, M. A. Hussein Sharif, K. State of Art for Semantic Analysis of Natural Language Processing. Qubahan Acad. J. 1, 21–28 (2021).
doi: 10.48161/qaj.v1n2a40
Motter, A. E., De Moura, A. P. S., Lai, Y. C. & Dasgupta, P. Topology of the conceptual network of language. Phys. Rev. E. 65, 065102 (2002).
doi: 10.1103/PhysRevE.65.065102
Ferrer i Cancho, R., Solé, R. V. & Köhler R. patterns in syntactic dependency networks. Phys. Rev. E. 69, 051915 (2004).
doi: 10.1103/PhysRevE.69.051915
Drieger, P. Semantic Network Analysis as a Method for Visual text analytics. Procedia Soc. Behav. Sci. 79, 4–17 (2013).
doi: 10.1016/j.sbspro.2013.05.053
Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, P10008 (2008).
doi: 10.1088/1742-5468/2008/10/P10008
Lu, H., Halappanavar, M. & Kalyanaraman, A. Parallel heuristics for scalable community detection. Parallel Comput. 47, 19–37 (2015).
doi: 10.1016/j.parco.2015.03.003
Moreo, A., Romero, M., Castro, J. L. & Zurita, J. M. Lexicon-based comments-oriented news sentiment Analyzer system. Expert Syst. Appl. 39, 9166–9180 (2012).
doi: 10.1016/j.eswa.2012.02.057
Xu, L., Lin, H., Pan, Y., Ren, H. & Chen, J. Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 27, 180–185 (2008).
Boson BosonNLP sentiment lexicon. https://bosonnlp.com/dev/resource/
Breiman, L., Random & Forests Mach. Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).
doi: 10.1007/BF00058655
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J. & Chica-Olmo, M. Rigol-Sanchez, J. P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67, 93–104 (2012).
doi: 10.1016/j.isprsjprs.2011.11.002
Strobl, C., Boulesteix, A. L., Zeileis, A. & Hothorn, T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8, 25 (2007).
doi: 10.1186/1471-2105-8-25
Doshi-Velez, F. & Kim, B. Considerations for evaluation and generalization in interpretable machine learning. in Explainable and Interpretable Models in Computer Vision and Machine Learning (eds Escalante, H. J. et al.) 3–17 (Springer International Publishing, Cham, (2018).
doi: 10.1007/978-3-319-98131-4_1
Hand, D. J. & Till, R. J. A simple generalisation of the Area under the ROC curve for multiple class classification problems. Mach. Learn. 45, 171–186 (2001).
doi: 10.1023/A:1010920819831
Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006).
doi: 10.1016/j.patrec.2005.10.010
Ghosh, I., Jana, R. K. & Abedin, M. Z. An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features. Int. J. Contemp. Hosp. Manag. 35, 3592–3611 (2023).
doi: 10.1108/IJCHM-05-2022-0562
Liu, X., Xie, F. & Wu, X. Graph based keyphrase extraction using LDA Topic Model. J. China Soc. Sci. Tech. Inf. 35, 664–672 (2016).
Zhang, J. Listening to the consumer: exploring review topics on Airbnb and their impact on Listing Performance. J. Mark. Theory Pract. 27, 371–389 (2019).
doi: 10.1080/10696679.2019.1644953
Gao, B., Zhu, M., Liu, S. & Jiang, M. Different voices between Airbnb and hotel customers: an integrated analysis of online reviews using structural topic model. J. Hosp. Tour Manag. 51, 119–131 (2022).
doi: 10.1016/j.jhtm.2022.03.004
Ding, K., Choo, W. C., Ng, K. Y. & Ng, S. I. employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation. Int. J. Hosp. Manag. 91, 102676 (2020).
doi: 10.1016/j.ijhm.2020.102676
Ju, Y., Back, K. J., Choi, Y. & Lee, J. S. Exploring Airbnb service quality attributes and their asymmetric effects on customer satisfaction. Int. J. Hosp. Manag. 77, 342–352 (2019).
doi: 10.1016/j.ijhm.2018.07.014
Ding, K., Choo, W. C., Ng, K. Y., Ng, S. I. & Song, P. Exploring sources of satisfaction and dissatisfaction in Airbnb Accommodation using unsupervised and supervised topic modeling. Front. Psychol. 12, 659481 (2021).
pubmed: 33967922
pmcid: 8096999
doi: 10.3389/fpsyg.2021.659481
Santos, G., Mota, V. F. S., Benevenuto, F. & Silva, T. H. Neutrality may matter: sentiment analysis in reviews of Airbnb, Booking, and couchsurfing in Brazil and USA. Soc. Netw. Anal. Min. 10, 45 (2020).
doi: 10.1007/s13278-020-00656-5
Guttentag, D. A. & Smith, S. L. J. Assessing Airbnb as a disruptive innovation relative to hotels: substitution and comparative performance expectations. Int. J. Hosp. Manag. 64, 1–10 (2017).
doi: 10.1016/j.ijhm.2017.02.003
Wang, C. & Wang, Q. Investigation and Promotion Strategy of Homestay tourists’ satisfaction in Tianjin: based on the modified IPA analysis Method. Sustain. Dev. 12, 1199–1207 (2022).
doi: 10.12677/SD.2022.124136
Jiang, Y., Balaji, M. S. & Jha, S. Together we tango: Value facilitation and customer participation in Airbnb. Int. J. Hosp. Manag. 82, 169–180 (2019).
doi: 10.1016/j.ijhm.2019.05.004
Liu, S. Q., Mattila, A. S. & Airbnb Online targeted advertising, sense of power, and consumer decisions. Int. J. Hosp. Manag. 60, 33–41 (2017).
doi: 10.1016/j.ijhm.2016.09.012
Zhang, L., Wei, W., Line, N. D. & Cheng, Y. When positive reviews backfire: the effect of review dispersion and expectation disconfirmation on Airbnb guests’ experiences. Int. J. Hosp. Manag. 96, 102979 (2021).
doi: 10.1016/j.ijhm.2021.102979
Adam, I. & Amuquandoh, F. E. Hotel characteristics and location decisions in Kumasi Metropolis, Ghana. Tour. Geogr. 16, 653–668 (2014).
doi: 10.1080/14616688.2012.762689
Jiao, J. & Bai, S. An empirical analysis of Airbnb listings in forty American cities. Cities. 99, 102618 (2020).
pubmed: 38282953
pmcid: 10817725
doi: 10.1016/j.cities.2020.102618
Stergiou, D. P. & Farmaki, A. Resident perceptions of the impacts of P2P accommodation: implications for neighbourhoods. Int. J. Hosp. Manag. 91, 102411 (2020).
doi: 10.1016/j.ijhm.2019.102411
Mei, L. & Jiang, H. The spatial differentiation process,factors and trends of B&B in Beijing Based on Airbnb Data. Econ. Geogr. 41, 213–222 (2021).