Online complaining behavior: Does cultural background and hotel class matter?

Sann, Raksmey; Lai, Pei‐Chun; Liaw, Shu‐Yi

doi:10.1016/j.jhtm.2020.02.004

Cited by 26 publications

(21 citation statements)

References 57 publications

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“…High cleanliness issues (cleanliness > 20.5), indicating a lot of complains about cleanliness, yield 91% negative reviews (computed based on negative cases over total cases on the leaf node). This abides with results from [37]. However, when mild cleanliness is combined with high professional staff attitude (Service/Staff > 12.5), the reviews are slightly positive (67%).…”

Section: Resultssupporting

confidence: 61%

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Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing

Christodoulou

Gregoriades

Pampaka

et al. 2020

Lecture Notes in Business Information Processing

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This paper applies a smart tourism approach to tourist destination marketing campaigns through the analysis of tourists' reviews from TripAdvisor to identify significant patterns in the data. The proposed method combines topic modelling using Structured Topic Analysis with sentiment polarity, information on culture, and purchasing power of tourists for the development of a Decision Tree (DT) to predict tourists' experience. For data collection and analysis, several custom-made python scripts were used. Data underwent integration, cleansing, incomplete data processing, and imbalance data treatments prior to being analysed. The patterns that emerged from the DT are expressed in terms of rules that highlight variable combinations leading to negative or positive sentiment. The generated predictive model can be used by destination management to tailor marketing strategy by targeting tourists who are more likely to be satisfied at the destination according to their needs.

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Section: Resultssupporting

confidence: 61%

“…However, when mild cleanliness is combined with high professional staff attitude (Service/Staff > 12.5), the reviews are slightly positive (67%). Staff professionalism and quality of service is a strong predictor of positive reviews with a weight of 93%, result also supported by [37].…”

Section: Resultsmentioning

confidence: 59%

Combination of Topic Modelling and Decision Tree Classification for Tourist Destination Marketing

Christodoulou

Gregoriades

Pampaka

et al. 2020

Lecture Notes in Business Information Processing

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“…Li and Ryan (2020) find that location in medium-class hotels acts as an excitement factor for international guests, and a performance factor for domestic guests, while for high-class hotels, location is a performance factor for all the guests. On the other hand, Sann et al (2020) find that the rating of location is the highest of the six attributes under analysis, and conclude that guests have a high level of satisfaction with respect to the location of the hotels in comparison with the rest of the attributes. It is interesting to have in mind that these results can be considered as average results for 404 TripAdvisor listed British hotels which present at least 20 negative reviews.…”

Section: Resultsmentioning

confidence: 89%

“…In this sense, cross-cultural differences do not only play a key role in tourist attractiveness but do also pose challenges in tourism industry development (Kim and McKercher 2011). In addition, Sann et al (2020) find evidence of the existing gap that still exists in the analysis of whether the relationship between complaints made by hotel guests with respect to some attributes depend on the guests' cultural background or the hotel classification.…”

Section: Introductionmentioning

confidence: 93%

“…In the hospitality area, cross-cultural consumer behaviour and its influence on satisfaction started only around the late 1980s (Richardson and Crompton 1988). More recently, analyses of how culture and language affect tourists' judgements in online reviews have been carried out (Alrawadieh and Law 2019;Cenni and Goethals 2017;Liu et al 2017;Sann et al 2020;Schuckert et al 2015). De Carlos et al (2018) extend the analysis taking also into consideration the country of origin, disentangling the fact that some tourists might have a second language to express the comments.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Nationality and Hotel Class on Guests’ Satisfaction. A Fuzzy-TOPSIS Approach Applied in Saint Petersburg

Martı́n

Rudchenko

Rebull

2020

Administrative Sciences

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Although hotels usually have clients from different nationalities, the research analyses the multicultural effects on hotel customers’ satisfaction is still scant. This paper aims to contribute to the realm of hotel management by providing interesting managerial insights into how different nationalities perceive hotel attributes differently using two hotels located in Saint Petersburg as a case study. To that end, a fuzzy hybrid method based on the technique of similarity to ideal solution (FTOPSIS) is proposed. The results are based on a sample of 447 guests and show that: (1) nationality influences the hotel guests’ satisfaction; (2) customers are, in general, more elastic in three-star hotels than in four-star hotels; (3) welcome gifts in the room and in the bathroom are key attributes in the clients’ satisfaction; and (4) Italian and Spanish guests are the least and the most satisfied customers, respectively. The study offers a number of important managerial insights to hotel managers and practitioners. The average figures obtained by general hotel satisfaction surveys usually hinder important peculiarities that need to be addressed when managers develop strategic satisfaction enhancement programmes. In particular, our results show that managers need to adapt the programmes to the differences observed by nationalities.

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