The purpose of this study is to explore consumer reviews with robotics based on the five dimensions for evaluating user experiences (i.e. embodiment, emotion, human-oriented perception, feeling of security and co-experience), as derived from research in human-robot interactions (HRI).,The study first reviews the five dimensions for evaluating user experiences in HRI and then analyzes user experiences with robotics at four hotels (i.e. Yotel New York, Aloft Cupertino, Henn-na Hotel Japan and Marriott Residence Inn LAX) based on reviews on TripAdvisor, Agoda, Yelp and Booking.com.,The findings highlight the influence of robotic embodiment and human-oriented perceptions on consumer experiences. The findings also suggest that users and robots can co-create novel experiences, with some guests even proactively seeking new opportunities to interact and communicate with robots to develop a certain level of “relationship” with them.,An understanding of user experiences from HRIs can inform future hospitality and tourism research and management.,This study contributes to hospitality and tourism management by highlighting current practices with robotics to suggest areas of improvements for enhancing future consumer experiences.,Consumer experiences will change rapidly as hospitality and tourism management deploys robotics in the future.,This is one of the early studies in the field to explore consumer experiences with robotics based on the five dimensions for evaluating user experiences from research in HRI. In doing so, this study provides a number of theoretical and managerial implications relevant for hospitality and tourism research and practice.

Exploring customer experiences with robotics in hospitality

In today’s world, social media is playing an indispensable role on the learning behavior of university students to achieve sustainable education. The impact of social media on sustainable education is becoming an essential and impelling factor. The world has become a global village and technology use has made it a smaller world through social media and how it is changing instruction. This original study is amongst the few to perform a focalized investigation on revealing the relationship between positive and negative characteristics of social media and the learning attitude of university students for sustainable education. However, this study aims to examine the constructive and adverse factors that impact on students’ minds and how these helped students to share positive and negative aspects with others. It is increasingly noticeable that social networking sites and their applications present enormous benefits for as well as risks to university students and their implications on students’ psychological adjustment or learning behaviors are not well understood. This study adapted the cluster sampling method, and respondents participated from five selected regions. Researchers distributed 1013 questionnaires among the targeted sample of university students with an age range of 16 to 35 years, and they collected 831 complete/valid responses. This study applied the social gratification theory to examine students’ behavior practicing social media usage. This study specifically identified 18 adversarial and constructive factors of social media from the previous literature. The findings revealed that the usage of social media in Pakistan has a negative influence on a student’s behavior as compared to positive aspects. Results may not be generalized to the entire student community as findings are specific to the specific respondents only. This study presents a relationship between antithetical and creative characteristics of social media and exhibits avenues for future studies by facilitating a better understanding of web-based social network use.

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

An investigation on online reviews in sharing economy driven hospitality platforms: a viewpoint of trust.

In this paper, we assess and compare the performance of French tourism destinations, using the Data Envelopment Analysis (DEA) two-stage procedure, where in the first stage the efficiency score are calculated, and then followed in the second stage by a bootstrapped truncated regression model. In the context of France such analysis takes an additional importance, especially as the country is expected to face a decrease in its tourism competitiveness. A discussion in terms of D-attraction and E-attraction is also proposed and policy recommendations are derived.

Performance of French destinations: Tourism attraction perspectives

Data from a visitor survey on the Danish Island of Bornholm were analyzed using multivariate techniques. Factor analysis of visitors’ preferences and behavior patterns indicated three main goals: relaxation, nature, and local culture. Two distinct clusters, “active” and “inactive” vacationers, were derived from the data. Actives tended to be Germans, to plan their vacation carefully to exercise autonomy in their booking of transport and accommodation, and to value the provision of amenities on the island. Inactives were mostly Scandinavians, and though they made fewer plans, they were more likely to visit the island’s most popular attractions. On the basis of logistic regression analysis, it is suggested that the most important differentiating factors between clusters were (1) the importance accorded to amenity provision, (2) nationality, (3) age, (4) activity participation, (5) planning behavior, (6) attraction visiting behavior, (7) autonomy, and (8) gender.

Market Segmentation and the Prediction of Tourist Behavior: The Case of Bornholm, Denmark

Homogeneity versus heterogeneity of cultural values: An item-response theoretical approach applying Hofstede's cultural dimensions in a single nation

Purpose




Online reviews have been gaining relevance in hospitality and tourism management and represent an important research avenue for academia. This study aims to illustrate the discrimination between positive and negative reviews based on single word items and the sector-specific relevance of hidden topics.




Design/methodology/approach




By probing two parallel approaches of entirely unrelated analytical methods (penalized support vector machines and Latent Dirichlet Allocation), the analysts explore differences in language between favorable and unfavorable reviews in three service settings (hotels, restaurants and attractions).




Findings




The percentage of correctly predicted positive and negative review reports by means of individual word items does not decrease if reports from the three tourism businesses are analyzed together.




Originality/value




However, there is limited generalizability of the discriminant words across the three businesses. Also, the latent topics relevant for generating customers’ review reports differ significantly between the three sectors of tourism businesses.

Exploring the generalizability of discriminant word items and latent topics in online tourist reviews

Hidden theorizing in big data analytics: With a reference to tourism design research

Discusses consumer lifestyle attributes and psychographic data with respect to market segmentation and as systematized by “Eurostyles”. Describes a sample application of a neural network model to assist in the transfer of the Eurostyle typology to the USA.

"Exporting" Eurostyles to the USA

The Internet in general and social media in particular have changed the way travellers search for information, interact and communicate with companies and among their peers. Through social media, travellers are not only content consumers but also producers who provide detailed evaluations of their experiences in a destination. Given the abundance of hotel reviews, managers need cost effective, time saving and scalable systems to identify relevant customer evaluations. This text mining study presents insights into the analysis of reviews posted on TripAdvisor. A penalized Support Vector Machine identifies keywords representative for the most positive and negative reviews. These terms can be used as an early warning system by managers to efficiently monitor the online dialogue on their hotel. The benefit of the suggested approach is the selection of the most important terms regarding the weight of the term and not regarding mere term frequency.

Josef A. Mazanec

Papers

Homogeneity versus heterogeneity of cultural values: An item-response theoretical approach applying Hofstede's cultural dimensions in a single nation

Exploring the generalizability of discriminant word items and latent topics in online tourist reviews

Hidden theorizing in big data analytics: With a reference to tourism design research

"Exporting" Eurostyles to the USA

Significant word items in hotel guest reviews: A feature extraction approach