A linguistic multi-criteria decision making methodology for the evaluation of tourist services considering customer opinion value
As a consequence of the exponential growth in online data, tourism sector has experimented a radical transformation. From this large amount of information, opinion makers can be benefited for decision making in their purchase process. However, it can also harm them according to the information they...
| Authors: | , , , , |
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| Format: | article |
| Publication Date: | 2020 |
| Country: | España |
| Institution: | Universidad Complutense de Madrid (UCM) |
| Repository: | Docta Complutense |
| Language: | English |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/114625 |
| Online Access: | https://hdl.handle.net/20.500.14352/114625 |
| Access Level: | Open access |
| Keyword: | 519.2 519.816 338.48 658.818 Fuzzy linguistic modeling Customer opinion value Multi-criteria decision-making Evaluation of tourist services Estadística Turismo Investigación operativa (Estadística) Marketing 5312.90 Economía Sectorial: Turismo 1209 Estadística 1207 Investigación Operativa 1209.04 Teoría y Proceso de decisión 5311.05 Marketing (Comercialización) |
| Summary: | As a consequence of the exponential growth in online data, tourism sector has experimented a radical transformation. From this large amount of information, opinion makers can be benefited for decision making in their purchase process. However, it can also harm them according to the information they consult. In fact, being benefited or harmed by the information translates into greater or lesser satisfaction after the purchase. This will largely depend on the published opinions that they take into account, which in turn depend on the value of the opinioner who publishes said information. In this paper, the authors propose a methodology that integrates multiple decision-making techniques and with which it is intended to obtain a ranking of hotels through the opinions of their past clients. To do this, the customer value is obtained using the Recency, Frequency, Helpfulness model. The information about the users found in the social networks is managed and aggregated using the fuzzy linguistic approach 2-tuples multi-granular. In addition, we have verified the functionality of this methodology by presenting a business case by applying it on TripAdvisor data |
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