Assessment of airport service quality: A complementary approach to measure perceived service quality based on Google reviews

A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service

“…To counter this weakness, online reviews have been recognized as a recent data source worthwhile to exploit. Various studies using reviews have demonstrated their value and usefulness in related research [14,17,25,26,41,45,52]. Online reviews are easily gathered with big data in real-time [22].…”

“…Online reviews are less likely to lead to statistical errors due to an insufficient level of sample representativeness, which is inevitable in typical survey-based sampling, because they are not sampled but fully collected given the collecting principle. Lee and Yu [41] proposed a method of measuring customer satisfaction using online reviews and ratings generated by airport users. Based on a sentiment analysis, Gitto and Mancuso [17] made effort to analyze traveler satisfaction making use of online reviews to upgrade the level of airport service.…”

“…After experiencing service, customers express their perception of service experiences and various aspects that make up the experiences in the form of user generated content [41,44,74]. User generated content generally consists of a brief assessment of overall service performance with numerical ratings, and a detailed description of the assessment as a form of textual reviews [52].…”

“…First, TAIPA extracts latent topics from customer-authored online reviews using the latent Dirichlet allocation (LDA). Since a topic is a probability distribution of word frequency from reviews enclosing passenger perceptions [22,[38][39][40][41], and the perceptions of service experiences are the sum of experience perceptions from all service encounters in the corresponding service steps [22,42], a topic itself or a group of topics with similar characteristics is well represented as a service encounter [22,43]. To include customer perceptions of the entire service process, TAIPA employs service encounters, in which customer service experiences occur and, thus, most customer perceptions are created, as a set of attributes composed of LDA topics that include direct perceptions of service experiences.…”

“…In addition, importance is adjusted via a sentiment analysis of online reviews to provide more precise measurements for customer experience perceptions. Third, TAIPA uses online ratings of customers as the performance of attributes because customers frequently expose service performance by means of rating values while perceiving the service [15,16,18,41,44,45]. The rationale of this process will be discussed in detail in Section 3.2.…”