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Hotel And Travel Index Classification System
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Industry Classification Benchmark (icb)
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Received: 12 November 2021 / Revised: 5 January 2022 / Accepted: 7 January 2022 / Published: 11 January 2022
As people make hotel booking decisions based on online research, how to improve customer ratings has become a key issue for hotel managers. Online reviews serve as a promising data source to improve service features to improve online bookings. This paper uses online customer ratings and written reviews to evaluate the bidirectional performance (good performance and positive reviews and poor performance and negative reviews) of hotel quality in terms of four-star ratings. . Sensitivity analysis is applied in combination with Kano’s model and importance performance analysis (IPA). Stratification and effect analysis methods were used to examine the bidirectional performance of hotel attributes in terms of four-star hotel ratings from 1,090,341 online reviews of hotels in London collected on TripAdvisor.com. (accessed on 4 January 2022). In particular, a new sentiment dictionary for the hospitality sector is built from multiple internet surveys using the PolarityRank algorithm to convert textual surveys into sentiment scores. The Kano-IPA model is applied to describe customer branding behavior and to prioritize features for improvement. The results provide the determinants of high/low customer ratings in different star hotels and suggest that hotel characteristics that contribute to high/low customer ratings vary in restaurant star rating soup. In addition, this paper analyzed the Kano section and the initial level of six hotel characteristics for each star rating of the hotel to design an improvement strategy. The theoretical and practical implications of these results are discussed at the end.
Determinants Of Tourists’ Length Of Stay
Unlike the recommendations of relatives and friends in the past, people are increasingly making hotel booking decisions based on internet research and various online travel sites. this time. Many customers post online reviews on hotels based on their experience with the hotel, which is perceived as more objective, reliable and helpful than the information provided by the hotel [1, 2]. Online reviews usually consist of online ratings and written reviews. Online ratings indicate customer satisfaction or dissatisfaction with a hotel. Written analysis contains expectations, feelings and perceptions of customers about hotel services. According to common sense principles, consumers cannot interpret and extract useful information from a wide variety of data, thus making them more interested in ranking than in text analysis [3]. . Since many customers may consider the online rating as one of the direct references of the quality of the hotel when choosing a hotel, it is important for the hotel to get the rating. high level of customers to achieve the goal of improving online booking [4, 5]. Therefore, exploring what makes the difference online between satisfied and dissatisfied customers is important for hotels. In other words, for the purpose of being permanently competitive in the hospitality industry, it is very important for hotels to understand the factors that determine customer satisfaction and dissatisfaction that depending on the online level [6, 7].
Current research has shown that the performance of many hotel attributes is related to customer satisfaction [8, 9, 10]. Many studies have investigated hotel characteristics that lead to customer satisfaction and dissatisfaction through research [11, 12, 13]. Recently, with the development of data collection methods, online surveys serve as a promising data source for customer satisfaction surveys. Many scholars have analyzed the brand identity through online research using the sentiment analysis method, and therefore determine the factors that determine customer satisfaction in the hotel industry [14, 15]. However, these studies designed hotel reviews as a comprehensive data collection, ignoring positive and negative observations. Conducting a general hotel survey can compare the performance of many attributes from the perspective of all customers but cannot distinguish between those that provide customer satisfaction and those that cause dissatisfaction. customer satisfaction. Previous studies have found that dual-valence (that is, expressing both positive and negative opinions) reviews in five-star hotels [16, 17]. The presence of a negative effect on the quality and positive analysis and a positive effect on the quality and negative analysis was observed [18, 19, 20]. In other words, even if the performance of many hotel attributes does not meet customer expectations, customers are still satisfied with the hotel and give a high rating to the hotel. due to the good performance of other qualities. Meanwhile, customers may not be satisfied with the hotel and give a low rating to the hotel when the performance of some hotel attributes is not good, although they think that other attributes works well. Therefore, it is important to solve these problems:
Which characteristics of high-performing hotels contribute to high customer ratings and which poor hotel characteristics result in low customer ratings?
In fact, it should be pointed out that customer expectations and opinions vary in different market segments, such as different star ratings of hotels [14, 21]. Exploring the determinants of customer satisfaction and dissatisfaction of each market segment is useful for making appropriate and accurate strategies [10]. Also, it helps hoteliers to understand what customers want for different star hotels and make the decision to enter a new market by analyzing the differences in quality and hotel style. different stars. However, whether hotel characteristics that contribute to high/low customer ratings vary across different hotels have not been confirmed. Therefore, this study aims to investigate the following questions:
Hotels With The Best Customer Service
In order to answer the above two questions, it is necessary to analyze the effect of personality traits on customer satisfaction. Customer preferences, expectations and understanding and characteristics of each hotel affect the overall factors, thus making positive and negative customer reviews work bidirectionally (good and bad quality) of restaurant quality [22]. Traditionally, one unit increase in positive performance and one unit decrease in negative performance regarding some hotel attribute should lead to one change in customer satisfaction, thus assuming a relationship between performance quality and customer satisfaction is linear or symmetrical [23]. However, some studies have shown that certain behaviors are more satisfying than unsatisfying [24, 25, 26]. In other words, hotel characteristics may have an asymmetric effect on customer satisfaction [24]. The Kano model was developed by Kano et al. (1984) to identify non-linear or asymmetric relationships between performance attributes and customer satisfaction. Kano’s model is often applied to classify hotel characteristics into different categories in relation to customer needs, which helps hotel managers better understand the expectations and perceptions of customers. market [27, 28]. Meanwhile, considering the limited hotel resources, it is very important to determine the characteristics of the needs to increase customer satisfaction through service improvement. Many studies have shown that applying the combination of the Kano model and the important performance analysis (IPA) in the customer satisfaction survey can not only analyze the customer’s requirements for service characteristics, but also determine the quality requirements [10, 29, 30, 31, 32, 33]. IPA is a common and effective method to develop improvement plans according to the importance and performance of the attribute [34]. However, existing studies on the Kano-IPA model are based on surveys, and few studies use Internet surveys as the data source for the Kano-IPA model. There are two main factors that limit the application of the Kano-IPA model in Internet research. Similarly, online text analysis is unstructured and therefore needs to be structured before it can be converted into structured data. On the other hand, there are questions about how to apply structured data and customer satisfaction models to achieve different Kano models. Considering that internet research serves as a promising data source for researching and improving hotel services, this study intends to apply feature extraction and natural language processing (NLP) to guide models Kano-IPA through internet research.
In summary, this study aims to identify the positive characteristics that contribute to a high number of customers and the negative characteristics that lead to low customer satisfaction.
