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Purpose Airbnb, a popular peer-to-peer accommodation platform, exceeds the yearly revenue of hotel chains, such as Marriot and Hilton. However, the reason why consumers engage with peer-to-peer accommodations and become loyal is not completely clear yet. This study aims to investigate Airbnb as a service setting. In doing so, more insights into the relevance of concepts, such as service quality (SQ) and hospitality factors, to explain consumers’ behavioral intentions with peer-to-peer accommodations and its effect on loyalty can be gained. Design/methodology/approach Through an online survey among Airbnb users and structural equation modeling, the model connecting the measurement constructs is analyzed. Findings This study shows that SQ and importance of having social and authentic experiences are significant antecedents of tourists’ loyalty toward Airbnb hosting services. Interestingly, perceived economic benefits do not impact the level of loyalty, neither does feelings of perceived reduce risk. Originality/value The theoretical contributions reveal tourists’ behavioral patterns in the peer-to-peer accommodation context influenced by standard service factors used for other types of accommodations. This study has particular implications for the accommodation sector when segmenting customers according to their needs and designing appropriate marketing strategies.

The emergence of peer-to-peer (P2P) accommodation (e.g. Airbnb) has steadily increased the pressure on the traditional accommodation sector. Although Airbnb listings are perceived as being more affordable than hotels, this has not yet been conclusively demonstrated. Therefore, the aim of this study is to investigate whether significant price dependencies exist between the Airbnb and traditional accommodation sectors and to analyze the underlying pricing strategies. For this purpose, the Estonian capital city of Tallinn is used as a case example. Airbnb data, prices and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest (POIs), and so on, are used in hedonic price regression models. The results show that Airbnb pricing positively depends on characteristics of the listing and the number of POIs within an optimal 650 m radius, which is obtained from a simulation study. Also, prices of hotels and of other Airbnb listings within the same radius positively impact Airbnb listing prices. Finally, Airbnb accommodations are shown to indeed be the more affordable alternative.

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Purpose Few studies to date have explored factors contributing to the dining experience from a visitor’s perspective. The purpose of this study is to investigate whether different restaurant attributes are critical in evaluating the restaurant experience in online reviews for visitors (non-local) and local guests. Design/methodology/approach In all, 100,831 online restaurant reviews retrieved from TripAdvisor are analyzed by using domain-specific aspect-based sentiment detection. The influence of different restaurant features on the overall evaluation of visitors and locals is determined and the most critical factors are identified by the frequency of their online discussion. Findings There are significant differences between locals and visitors regarding the impact of busyness, payment options, atmosphere and location on the overall star rating. Furthermore, the valence of the factors drinks, facilities, food, busyness and menu found in the reviews also differs significantly between the two types of guests. Practical implications The findings of this study help restaurant managers to better understand the different customer needs. Based on the results, they can better decide which restaurant aspects should receive the most attention to ensure that customers are satisfied. Originality/value Research on online reviews has largely neglected the role of different visitation motives. This study assumes that the reviews of local and non-local restaurant visitors are based on different factors and separates them to gain a more fine-grained and realistic picture of the relevant factors for each particular group.

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