Exploring the user-generated content data to improve quality management

Abstract

PurposeThe focus of this research is to investigate if user-generated content (UGC) generated in the Booking platform can support quality management improvement within the hospitality industry by increasing customer satisfaction and eliminating defects more efficiently. Hence, it contributes to understanding how data-driven companies can rely on customer data to focus on innovation and performance improvement to meet customer requirements, eliminate defects and increase customer satisfaction.Design/methodology/approachFollowing the literature review, information was collected from the digital platform Booking, encompassing 15 hotel industry companies in Portugal Porto and Braga regions, selected due to their high number of customer reviews. This data was organized and categorized, eliminating all unnecessary information for the research and building an Excel database. The database was subsequently analysed with SPSS and Voyant software, performing statistical analysis, hypothesis testing and text-mining techniques to analyse the comments. After these analyses, applying quality tools allowed for more in-depth conclusions.FindingsThe research results highlight that customers' most relevant requirements in the Portuguese hospitality industry are breakfast, parking and a swimming pool. It was also possible to realize that the location is an attractive requirement, the bathroom is a must-be requirement and breakfast is a performance requirement. The results also allowed us to answer the most critical research question: “Is user-generated content a valuable aid to quality?” the answer is yes since it was possible to use the data to find improvements and faults/failures in the services.Originality/valueThe results of this study represent an essential step towards a complete understanding of how to take advantage of UGC within the hospitality industry by establishing a solid base of techniques, methods and quality tools for UGC analysis that can be applied in future research on different industry sectors.