Effects of the booking.com rating system: bringing hotel class into the picture

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 left-skewed. Moreover, we find that the degree of skewness is positively associated with hotel class: lower-class hotels exhibit distributions of ratings that are statistically less skewed than higher-class hotels

Essays on credit risk

The work investigates two major topics: the presence of a systematic and an idiosyncratic component in CDS spreads and the credit spread puzzle. We verify that a systematic factor is priced in the cross-section of CDS returns. We also notice that the systematic component of risk increases after the financial crisis. We finally verify that the fraction of systematic risk is not the same in different industrial sectors. In particular, more cyclical and systemic sectors show a much larger impact of the systematic factor. Regarding the second topic, we extend the literature proposing a bivariate state space model and verify that it actually improves the performances of standard inversion techniques in explaining the observed credit spreads. The improvement is particularly significant during the crisis period, characterized by a larger noise contaminating the observed equity price and equity volatility. This supports the ability of the state space model to remove the noise component and to produce better estimates of the asset value of the company and, consequently, more accurate predictions of spreads. In the last chapter we identify some explicit drivers for the noise postulated in the second paper. In particular, we verify that the errors produced by structural credit risk models significantly depend on liquidity indicators and that their explained variability is not negligible. We finally verify that the errors left by both structural variables and liquidity indicators are strongly correlated with market-wide measures of limits of arbitrage and/or deleveraging pressures

Online reviews: differences by submission device

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 role of language in the online evaluation of hospitality service encounters: an empirical study

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

A Double Siamese Framework for Differential Morphing Attack Detection

Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results

Are environmental-related online reviews more helpful? A big data analytics approach

Purpose–Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this study explores if and to what extent environmental discourse embedded in ORs has an impact on electronic Word-of-Mouth (e-WOM) helpfulness across 8 major destination cities in North America and Europe. Design/methodology/approach–This study gathered, by means of Big Data techniques, 2.7 million online reviews (ORs) hosted on Booking and TripAdvisor, and covering hospitality services in 8 different destinations cities in North America (New York City, Miami, Orlando, and Las Vegas) and Europe (Barcelona, London, Paris, and Rome) over the period 2017-2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness. Findings–The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers. Research implications/limitations – Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse. Originality/value – This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM, and sustainable tourism research

Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics

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

Exploring environmental concerns on digital platforms through big data: the effect of online consumers’ environmental discourse on online review ratings

By deploying big data analytical techniques to retrieve and analyze a large volume of more than 2.7 million reviews, this work sheds light on how environmental concerns expressed by tourists on digital platforms, in the guise of online reviews, influence their satisfaction with tourism and hospitality services. More specifically, we conduct a multi-platform study of Tripadvisor.com and Booking.com online reviews (ORs) pertaining to hotel services across eight leading tourism destination cities in America and Europe over the period 2017–2018. By adopting multivariate regression analyses, we show that OR ratings are positively influenced by both the presence and depth of environmental discourse on these platforms. Theoretical and managerial contributions, and implications for digital platforms, big data analytics (BDA), electronic word-of-mouth (eWOM) and environmental research within the tourism and hospitality domain are examined, with a view to capturing, empirically, the effect of environmental discourse presence and depth on customer satisfaction proxied through online ratings

Online review helpfulness and firms’ financial performance: an empirical study in a service industry

The purpose of this study is to bridge a research gap in the electronic word-of-mouth (eWOM) literature: measuring the effect of the degree of online review helpfulness (ORH) on firms’ financial performance. As studies of the impact of ORH on firm performance in the context of service industries in general and more specifically in the hospitality sector are virtually non-existent, this work intends to offer insights to eWOM researchers by analyzing if and to what extent ORH affects the financial performance of hospitality firms. Based on a re-visitation of the antecedents of ORH stemming from information adoption models, social influence theory and dual process theory, we analyze the moderating effects of the degree of ORH on the relationships between online review valence/volume and firms’ financial performance. Based on the examination of 395,964 online reviews (ORs) related to 261 higher-end hotels located in London, the third most visited destination worldwide, we find that the degree of ORH positively moderates the positive effect of OR valence on financial performance, while it does not moderate significantly the positive effect of OR volume on financial performance. Theoretical contributions to the nascent research stream taking an outcome-oriented approach to the study of eWOM helpfulness and managerial implications are discussed

Face-from-Depth for Head Pose Estimation on Depth Images

Depth cameras allow to set up reliable solutions for people monitoring and behavior understanding, especially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only. A head detection and localization module is also included, in order to develop a complete end-to-end system. The core element of the framework is a Convolutional Neural Network, called POSEidon+, that receives as input three types of images and provides the 3D angles of the pose as output. Moreover, a Face-from-Depth component based on a Deterministic Conditional GAN model is able to hallucinate a face from the corresponding depth image. We empirically demonstrate that this positively impacts the system performances. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Experimental results show that our method overcomes several recent state-of-art works based on both intensity and depth input data, running in real-time at more than 30 frames per second