Determining Strategic Attributes of Hotel Satisfaction from TripAdvisor Data with Mix Method Dynamics

There is a need to analyze and interpret the impact of prominent hotel attributes in the hospitality industry on customer satisfaction. In this study, a two-stage mixed method is proposed to determine the impact of hotel attributes on customer satisfaction, in favor of mixing both qualitative and quantitative research paradigms. In the qualitative stage of the research, a data mining framework was developed by collecting a total of 710,450 customer reviews and scores of 1,132 hotels in different geographical regions of Turkey from the TripAdvisor website. Big data analysis methods were applied and a new attribute selection method based on distribution ratio generation that has not been included in the literature on online review works before was integrated into this framework. Approximately 70 attributes were clustered and discovered by the proposed method. To examine the effects of these attributes, an Importance–Performance Analysis (IPA) was conducted with 554 participants in the quantitative stage. The experimental results showed that the order of importance of the features revealed in this two-stage qualitative/quantitative research conducted with mixed method dynamics overlapped at a rate of 83%. Therefore, we conducted a methodological approach that will assist the hotel managers’ marketing strategies by promoting expertise-level use of big data by the proposed model.