1,721,316 research outputs found
“Taberina” bingistani Henson, 1948 (Foraminifera) from the upper Cenomanian of Apulia (Southern Italy): a new record.
No full textThe first occurrence of “Taberina” bingistani Henson, 1948 from Upper Cenomanian limestones of the Apulian platform cropping out near Polignano a Mare is recorded. The stratigraphic and paleogeographic range of this species, whose generic assignment is still doubtful, are discussed
Are environmental-related online reviews more helpful? A big data analytics approach
Full text linkPurpose: Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe. Design/methodology/approach: This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight 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 limitations/implications: 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
Full text linkPurpose: 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
Rassegna di sentenze e decisioni della Corte EDU rilevanti in materia penale – Maggio 2011
No full textExploring environmental concerns on digital platforms through big data: the effect of online consumers’ environmental discourse on online review ratings
Full text linkBy 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
Asymmetrical Influences of Service Robots’ Perceived Performance on Overall Customer Satisfaction: An Empirical Investigation Leveraging Online Reviews
Full text linkService scholars seem to have empirically overlooked the impact of service robots in the overall customer evaluation of tourism services. This study addresses this gap by leveraging three-factor theory and electronic Word-Of-Mouth data to assess human-robot interaction’ influence on customer satisfaction. Text analytics are deployed alongside a penalty-reward contrast technique on almost 70,000 online reviews spanning 44 hotels worldwide that incorporated service robots into their operations. Customer satisfaction with hospitality services is significantly increased by positive service robots’ performance, while no significant effect is associated with negative service robots’ performance. The traveler type does not moderate the relationship between service robots’ performance and customer satisfaction. These findings, confirmed through Propensity Score Matching, reveal that service robots constitute an “excitement factor” in hospitality service offerings, thus providing a strong incentive for their integration into the workforce. Policymakers are urged to proactively facilitate the transition to a more automated service economy
The role of emotions in the consumer meaning-making of interactions with social robots
Full text linkThe interaction with social robots is supposed to be a unique and emotionally charged activity. Based on the diffusion of innovations literature, subjective feelings represent a driver of the innovation diffusion process. Yet, to date, no study has comprehensively assessed consumers' emotional responses over time to interactions with social robots. Thus, the study aims to address this research gap by combining innovation diffusion and psy-chology literature. The emotional content of customers' self-reported communication on social robots deployed across international hotels is categorized through Plutchik's wheel of emotions by using advanced text analytics techniques to track and analyze its evolution over time. Findings show that consumers generally express positive emotions towards social robots. Trust, anticipation and joy are the most frequently expressed emotions. Empirical results from multivariate regression analysis indicate that joy has the greatest magnitude and that anticipation and surprise do not significantly influence consumers' opinions and comments. Negative emotions are less frequent but have a significantly negative impact, which might be considered by hotel managers willing to introduce social robots
Service robots in online reviews: Online robotic discourse
Full text linkPrevious research has suggested that customers' discourse in online reviews (ORs) is critical upon and immediately after new
product introductions to build consumers' awareness (Godes & Mayzlin, 2004) and can be leveraged to track the popularity of a
product or service feature over time (Chevalier & Mayzlin, 2006). Therefore, we argue that online consumers' discourse can be a
useful means to assess whether the awareness about service robots goes beyond a mere “novelty effect” (Roehrich, 2004). Indeed, if
the novelty effect associated with service robots is the only mechanism in place, robots' popularity would rapidly fade away, suggesting
that robots are not a distinctive factor in the evaluation of the service offering. As service robots have been introduced in
hospitality services quite recently, there is an urgent need to explore how and if customers' discourse revolving around them is
evolving over time from both a diffusion and adoption of innovation (Rogers, 2003) and a human-robot interaction theoretical
perspectives (Newell & Card, 1985; Tussyadiah, 2020).
To bridge this research gap, this study aims to provide preliminary insights on the following research question: Are service robots
becoming an increasingly distinctive and popular feature in hotel-related eWOM beyond their introduction? To this end, we develop th
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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