This study examines the role played by submission devices (mobile vs. desktop) in online travel reviewing behaviour. We analyse over 1.2 million online reviews from Booking.com and detect the presence and distinctive features of online reviews submitted by mobile devices. Our findings indicate that 1) the share of online reviews submitted by mobile increased at a very high rate over time (higher than the growth rate of those submitted by desktop); 2) there is a systematic and statistically significant difference between the features and distributions of online reviews submitted through mobile devices vs. online reviews submitted through desktops. We raise awareness of the role played by submission devices in online travel behaviour research and present implications for future research.

The "industry 4.0" phenomenon is expected to influence almost every aspect of business value chains, and hence it has been increasingly analyzed by management scholars. However, the overarching intellectual structure emerging from this new stream of literature has not yet been synthesized in a framework nor critically discussed. Furthermore, despite being part of the rhetoric in several recent industrial governmental plans, industry 4.0 in service sectors has not been systematically reviewed to date. By leveraging a systematic quantitative literature review, a data-driven approach and a quantitative methodology-embedding both bibliographic coupling and network analysis techniques-this study provides a clear visualization of the emerging intellectual structure of industry 4.0 in management studies. We also develop a framework based on the most recurrent themes emerging from the results of bibliometric and network analyses-the latter could be used by management scholars to understand studies surrounding industry 4.0. As service businesses can create and capture value generated through the 4 th Industrial Revolution as well as manufacturing firms, we suggest that scholarly attention should also be directed toward the service industries and provide a research agenda.

In an increasingly global travel market, hospitality services encounters involve growing interactions between providers and customers often belonging to different nationalities and cultures and speaking different languages. Extant hospitality management literature has explored the influence of language on service evaluations mostly in offline settings. This study innovatively captures the effect of the language used in online hotel reviews on online consumer ratings in two distinctively different destinations located in culturally different countries: Italy and Russia. Based on almost half a million Booking.com online reviews written by hotel guests in Moscow and Rome, we illuminate if and to what extent domestic vs. foreign language use affects online customer satisfaction. We find that the use of domestic language exerts a positive impact on online ratings in both countries. Implications for hospitality practitioners and managers, developers and managers of online review platforms, and customers of hotel services are discussed.

The purpose of this study is to continue the discussion initiated by Mellinas et al. (2015, 2016) on the effects of the Booking.com rating system and more widely on the use of the OTA as a data source in academic tourism and hospitality research. We enrich and complement the original work by Mellinas et al. (2015) by empirically investigating the effects of the Booking.com rating system on the distribution of hotel ratings for the overall population of hotels located in London over two years. Based on more than 1.2 million online reviews, we show that the overall distribution of hotel scores is significantly leftskewed. Moreover, we find that the degree of skewness is positively associated with hotel class: lowerclass hotels exhibit distributions of ratings that are statistically less skewed than higher-class hotels.

Purpose This paper aims to analyze if and to what extent mechanical artificial intelligence (AI)-embedded in hotel service robots-influences customers’ evaluation of AI-enabled hotel service interactions. This study deploys online reviews (ORs) analytics to understand if the presence of mechanical AI-related text in ORs influences customers’ OR valence across 19 leading international hotels that have integrated mechanical AI – in the guise of service robots – into their operations. Design/methodology/approach First, the authors identified the 19 leading hotels across three continents that have pioneered the adoption of service robots. Second, by deploying big data techniques, the authors gathered the entire population of ORs hosted on TripAdvisor (almost 50,000 ORs) and generated OR analytics. Subsequently, the authors used ordered logistic regressions analyses to understand if and to what extent AI-enabled hospitality service interactions are evaluated by service customers. Findings The presence of mechanical AI-related text (text related to service robots) in ORs influences positively electronic word-of-mouth (e-WOM) valence. Hotel guests writing ORs explicitly mentioning their interactions with the service robots are more prone to associate high online ratings to their ORs. The presence of the robot’s proper name (e.g., Alina, Wally) in the OR moderates positively the positive effect of mechanical AI-related text on ORs ratings. Research limitations/implications Hospitality practitioners should evaluate the possibility to introduce service robots into their operations and develop tailored strategies to name their robots (such as using human-like and short names). Moreover, hotel managers should communicate more explicitly their initiatives and investments in AI, monitor AI-related e-WOM and invest in educating their non-tech-savvy customers to understand and appreciate AI technology. Platform developers might create a robotic tag to be attached to ORs mentioning service robots to signal the presence of this specific element and might design and develop an additional service attribute that might be tentatively named “service robots.” Originality/value The current study represents the first attempt to understand if and to what extent mechanical AI in the guise of hotel service robots influences customers’ evaluation of AI-enabled hospitality service interactions.