Chapter Using eye-tracking to evaluate the viewing behavior on tourist landscapes

Every tourist website employs images to attract potential tourists. In particular, destination tourism websites use environmental images, such as landscapes, to attract the attention of tourists and to address their purchase choice. Nowadays the effectiveness of these tools has been enhanced by the use of eye-tracking technology. That allows measuring the exact eye position during the visualization of images, texts, or other visual stimuli. Consequently, eye-tracking data can be processed to obtain quantitative measures of viewing behavior that can be analyzed for several purposes in many fields such as to cluster consumers, to improve the effectiveness of a website and for neuroscience studies. This work is aimed to use eye-tracking technology to investigate user behavior according to different types of images (e.g. natural landscapes, city landscapes). Specifically, we compare different statistical descriptive tools with supervised and unsupervised models. Furthermore, we discuss the effectiveness of their results and their capacity to provide satisfactory and interpretable solutions that can be used by decision-makers

Price indicators for Airbnb accommodations

New forms of hospitality grew increasingly more popular and successful during the last decades. Nowadays, they are chosen for different reasons, one of the most important certainly being price. Understanding the elements that can impact on price determination is crucial to increase profitability. We propose two price indicators for Airbnb accommodations, which are defined in three phases using proportional odds model as a reference model. The first phase focuses on the probability estimation of accommodations belonging to a specific class of price. The second phase aims to evaluate the ability of the model to make good predictions by computing three different indexes. Finally, the three indexes are combined to define the indicators q and r which evaluate, respectively, the impact that six different dimensions (transports, culture, crowd, property, management, and time) have with respect to price determination on Airbnb accommodations and their relative importance concerning neighborhoods. The analysis is focused on 61 neighborhoods of Rome. The findings show differences with respect to the impact of the dimensions on price for each neighborhood of Rome

VGLM proportional odds model to infer hosts’ Airbnb performance

We investigated aspects of host activities that influence and enhance host performance in an effort to achieve best results in terms of the occupancy rate and the overall rating. The occupancy rate measures the percentage of reserved days with respect to available days. The overall rating identifies the satisfaction level of guests that booked an Airbnb accommodation. We used the proportional odds model to estimate the impact of the managerial variables and the characteristics of the accommodation on host performance. Five different levels of the occupancy and the overall rating were investigated to understand which features impact them and support the effort to move from the lowest to the highest level. The analysis was carried out for Italy’s most visited cities: Rome, Milan, Venice, and Florence. We focused on the year 2016. Moreover, we investigated different impact levels in terms of the overall rating during the COVID-19 pandemic to evaluate possible differences. Our findings show the relevance of some variables, such as the number of reviews, services, and typology of the rented accommodation. Moreover, the results show differences among cities and in time for the relevant impact of the COVID-19 pandemic

Markov chain to analyze web usability of a university website using eye tracking data

Web usability is a crucial feature of a website, allowing users to easily find information in a short time. Eye tracking data registered during the execution of tasks allow to measure web usability in a more objective way compared to questionnaires. In this work, we evaluated the web usability of the website of the University of Cagliari through the analysis of eye tracking data with qualitative and quantitative methods. Performances of two groups of students (i.e., high school and university students) across 10 different tasks were compared in terms of time to completion, number of fixations and difficulty ratio. Transitions between different areas of interest (AOI) were analyzed in the two groups using Markov chain. For the majority of tasks, we did not observe significant differences in the performances of the two groups, suggesting that the information needed to complete the tasks could easily be retrieved by students with little previous experience in using the website. For a specific task, high school students showed a worse performance based on the number of fixations and a different Markov chain stationary distribution compared to university students. These results allowed to highlight elements of the pages that can be modified to improve web usability

Value Co-Destruction: a Text-Mining-Based Mixed Method Study on Social Media Interactions

To better understand how big data interconnects firms and customers, we analyse the role of customers’ emotions in the process of value co-destruction in a social media context. We perform a text mining based algorithm capable of identifying anger, expectation, disgust, fear, and sadness in peaks of problematic social interactions. The developed algorithm associated with an in-depth qualitative analysis shows how to employ unstructured big data to understand the role of negative emotions in the process of value co-destruction

A scientometric analysis of the effect of COVID-19 on the spread of research outputs

The spread of the COVID-19 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics related to different aspects of COVID-19. Italy has been one of the first countries to be massively involved in the outbreak of the disease. In this paper, we present an extensive scientometric analysis of the research production both at global (entire literature produced in the first 2 years after the beginning of the pandemic) and local level (COVID-19 literature produced by authors with an Italian affiliation). Our results showed that US and China are the most active countries in terms of number of publications and that the number of collaborations between institutions varies depending on geographical distance. Moreover, we identified the medical-biological as the field with the greatest growth in terms of literature production. As regards the analysis focused on Italy, we have shown that most of the collaborations follow a geographical pattern, both externally (with a preference for European countries) and internally (two clusters of institutions, north versus center-south). Furthermore, we explored the relationship between the number of citations and variables obtained from the data set (e.g. number of authors). Using multiple correspondence analysis and quantile regression we shed light on the role of journal topics and impact factor, the type of article, the field of study and how these elements affect citations

Arthroscopically assisted reduction and internal fixation (ARIF) versus open reduction and internal fixation (ORIF) for lateral tibial plateau fractures: a comparative retrospective study

Background: This study aims to explore if the arthroscopically assisted reduction and internal fixation (ARIF) technique is superior to the traditional open reduction and internal fixation (ORIF) technique in the treatment of tibial lateral plateau fractures. Methods: Forty patients with tibial plateau fractures (Schatzker type I–III) treated with ARIF or ORIF from 2012 to 2017 were included in this retrospective study. All patients received pre-operative radiographs and CT scans. The patients were divided into two groups (ARIF or ORIF). All patients had a minimum follow-up of 12 months and an average follow-up of 44.4 months. The clinical and radiographic outcomes were evaluated according to the Knee Society Score (KSS) and the modified Rasmussen radiological score. Results: Satisfactory clinical and radiological results were found in 39 out of 40 (97.5%) patients. KSS and modified Rasmussen radiological score were significantly better in ARIF group. The mean KSS was 92.37 (± 6.3) for the ARIF group and 86.29 (± 11.54) for the ORIF group (p < 0.05). The mean modified Rasmussen radiographic score was 8.42 (± 2.24) for the ARIF group and 7.33 (± 1.83) for the ORIF group (p = 0.104). Worst clinical and radiological results were related to concomitant intra-articular lesions (p < 0.05). Meniscal tears were found and treated in 17 out of 40 (42.5%) patients. The overall complication rate was 10%. Conclusions: Both ARIF and ORIF provided a satisfactory outcome for the treatment of Schatzker I–III tibial plateau fractures. However, ARIF led to better clinical results than ORIF. No statistically significant differences were found in perioperative complications, radiological results, and post-traumatic knee osteoarthritis. Level of evidence: Level II

Reliability and reproducibility of the new AO/OTA 2018 classification system for proximal humeral fractures: a comparison of three different classification systems

Background: The classification systems for proximal humeral fractures routinely used in clinical practice include the Neer and Arbeitsgemeinschaft für Osteosynthesefragen/Orthopaedic Trauma Association (AO/OTA) 2007 systems. Currently used systems have low inter- and intraobserver reliability. In 2018, AO/OTA introduced a new classification system with the aim of simplifying the coding process, in which the Neer four-part classification was integrated into the fracture description. The aim of the present work is to assess the inter- and intraobserver agreement of the new AO/OTA 2018 compared with the Neer and AO/OTA 2007 classifications. Materials and methods: A total of 116 radiographs of consecutive patients with proximal humeral fracture were selected and classified by three observers with different levels of experience. All three observers independently reviewed and classified the images according to the Neer, AO/OTA 2007, and new AO/OTA 2018 systems. To determine the intraobserver agreement, the observers reviewed the same set of radiographs after an interval of 8 weeks. The inter- and intraobserver agreement were determined through Cohen’s kappa coefficient analysis. Results: The new AO/OTA 2018 classification showed substantial mean inter- (k=0.67) and intraobserver (k=0.75) agreement. These results are similar to the reliability observed for the Neer classification (interobserver, k=0.67; intraobserver, k=0.85) but better than those found for the AO/OTA 2007 system, which showed only moderate inter- (k=0.57) and intraobserver (k=0.58) agreement. The two more experienced observers showed better overall agreement, but no statistically significant difference was found. No differences were found between surgical experience and agreement regarding specific fracture types or groups. Conclusions: The results showed that the Neer system still represents the more reliable and reproducible classification. However, the new AO/OTA 2018 classification improved the agreement among observers compared with the AO/OTA 2007 system, while still maintaining substantial descriptive power and simplifying the coding process. The universal modifiers and qualifications, despite their possible complexity, allowed a more comprehensive fracture definition without negatively affecting the reliability or reproducibility of the classification system. Level of evidence: Level III, diagnostic studie