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This study aims to propose a data-driven approach, based on open-source tools, that makes it possible to understand customer satisfaction of the accommodation offer of a whole country.

The method starts by extracting information from all hotels of Portugal available at TripAdvisor through Web scraping. Then, a support vector machine is adopted for modeling the TripAdvisor score, which is considered a proxy of customer satisfaction. Finally, knowledge extraction from the model is achieved using sensitivity analysis to unveil the influence of features on the score.

The model of the TripAdvisor score achieved a mean absolute percentage error of around 5 per cent, proving the value of modeling the extracted data. The number of rooms of the unit and the minimum price are the two most relevant features, showing that customers appreciate smaller and more expensive units, whereas the location of the hotel does not hold significant relevance.

National tourist offices can use the proposed approach to understand what drives tourists’ satisfaction, helping to shape a country’s strategy. For example, licensing new hotels may take into account the unit size and other characteristics that make it more attractive to tourists. Furthermore, the procedure can be replicated at any time and in any country, making it a valuable tool for data-driven decision support on a national scale.

Considering the importance of the content created by the host for Airbnb consumers while making purchasing decisions, this study aims to analyze how the Airbnb hosts promote their properties by revealing the predominant attributes considered by hosts when advertising them.

The unstructured textual content of online Airbnb accommodations advertisements (property descriptions) is analyzed through a longitudinal text mining approach. This study defines a pipeline based on a topic modeling approach that allows not only to identity the most prevalent text attributes but also its distribution through time.

This research identifies and characterizes the attributes most advertised over time, on about 30,000 accommodations posted monthly over two years, between 2018 and 2020. Five main topics were identified in the data reflecting only pull motivations. Noteworthy is the slight changes in properties’ descriptions topics along the two years, suggesting that “service” is increasingly being perceived by hosts as an important attribute of Airbnb guest experience.

Through a text analysis, this study provides an insight into peer-to-peer accommodation on the key attributes that hosts consider in the description of their properties to leverage the attractiveness of Airbnb. In the light of existing research, which has predominantly focused on the trustworthiness and attractiveness of the Airbnb advertisement, this research differentiates by analyzing the main attributes in text over time. Given the Airbnb’s changes since its inception, a longitudinal view is relevant to clarify how hosts advertise their properties and how it evolves in the light of these changes.

This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.

This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.

Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.

This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

This paper aims to explore the impact of the origin of hops on the sensory and hedonic evaluation of highly involved craft beer consumers.

Data was collected through a between-subject tasting experiment, where the origin of hop was manipulated (imported vs locally grown). The craft beer samples used in the experiment were produced using hops of similar age but grown in two distinct places: USA (imported hop) and in the city of Ribeirão Preto, Brazil, where the experiment was conducted (locally grown hop). The sensory and hedonic evaluations of highly involved craft beer consumers (n = 100) were collected after tasting the samples.

The origin of hop proved to be significant in affecting participants’ sensory and hedonic evaluations. It was observed that women were more sensitive than men to the origin information: when information was given, differences were found only on men’s scores of dryness/astringency; while in women, significant differences were found not only in dryness/astringency, but also in bitterness and refreshing, which are important attributes in the sensory profile of craft beer. It was also confirmed the effect of localness in hop cultivation, once men’ and women’s scores on liking were higher for the sample brewed with locally grown hop.

To the best of the authors’ knowledge, this is the first work using craft beer brewed with hops cultivated in Brazil and a sample of Brazilian craft beer consumers, therefore, providing a significant contribution to the field of consumer behavior. Furthermore, it adds to the discussion on sex-/gender-related differences regarding sensory expectation and perception of foods.

A region can be registered as a geographical indication (GI) when its human or natural characteristics influence product attributes. However, GI registration is a complex process that can lead applicants to conflicts and resource waste. Since metabolomics can support product typicality, it can be an effective tool to evidence the place-product link, essential for GI registration. With the aim of increasing GI registration efficiency, this research aimed to map the Brazilian GI registration process from end to end, using business process model and notation (BPMN), and evaluate the application of metabolomics techniques as facilitator.

A qualitative exploratory study was proposed, with the Brazilian GI registration process as unit of analysis. Primary data was obtained through twelve in-depth interviews with GI producers, members of supporting institutions and metabolomics experts. Based on the collected data, the complete GI registration map was designed with bpmn.io software.

Results provided a clear view of the GI registration process. Interviewees considered the selection of the appropriate GI category, the establishment of GI common rules, product specification and the confirmation of the place-product link as the most demanding requirements. Since the last two involve specifying the influence of the origin on products, the use of metabolomics was suggested as a powerful tool to complete these stages.

The theoretical contribution is provided by the interweave of the three scientific fields mentioned (GI, business process management (BPM) and metabolomics) to contribute to the still small body of literature on GI-related processes. Additionally, other theoretical contributions are the presentation of a GI registration map associated to the use of “omics” techniques as process facilitator.

The practical contribution of this work is the establishment of an end-to-end view of the entire GI registration process, which makes easier for applicants to engage, plan resources and achieve the GI label. Furthermore, metabolomics acts as a facilitator since its data can be used for different purposes on the way to GI registration.

Results can be used to facilitate GI registration, reducing resources demanded from applicants. GI labels provide product protection, better selling prices and competitive advantages for producers, which are positive outcomes in countries with a commodity-based economy like Brazil.

The originality of the study lies in the approach used to map the GI registration process, as well as the suggestion of metabolomics as facilitator in some of its stages.

The purpose of this paper is to apply PROMETHEE II, a multicriteria decision-making method, to evaluate wineries, aiming to support the tourist’s decision regarding an online recommendation system on which wineries to prioritise visiting.

This study was divided into four phases: literature review, which aimed to identify a list of criteria related to the quality of services offered by wineries in specialised literature; validation of the categories and criteria with specialists in the field of wine tourism using a questionnaire with questions based on the stage of the research; multicriteria evaluation of wineries using PROMETHEE II method which was adjusted to the model through a pre-test with ten specialist professionals from different states (Rio Grande do Sul, Santa Catarina, Paraná, São Paulo and Pernambuco); and validation of the model with one decision-maker representing each winery in Rio Grande do Sul, Brazil, and a decision-making expert who is familiar with all the wineries.

These findings reveal various scenarios based on different criteria that wine tourists may consider, with emphasis on the terroir of the region, extensive tasting, picnics in the vineyards and improvement in the development of wine tourism with a night harvest. The core of wine tourism services is based on 12 dimensions: waiting for the service, ease of purchase, opening hours, price, winery landscape, variety of products, local cuisine, quality of tasted products, access to the winery, tourist facilities, sustainable products and hygiene installation.

This research contributes to the literature by demonstrating the applicability of multicriteria tools to solve wine tourism services based on the tourist perspective of the service. Still, this paper proposes that it be applied to other case studies.

This paper aims to propose a multicriteria model for the evaluation of tourist service quality in Brazilian wineries from a tourism perspective.

The model is comprising two phases: structure of the problem and application of the method. First, the selection of wineries in a given region, the identification of decision-makers that will perform the evaluation according to a set of 19 criteria based on the Tourqual protocol and the construction of the evaluation matrix in the next phase. Then, the analytic hierarchy process (AHP) method is applied and a rank of wineries is provided.

The model is applied to evaluate the seven most important wineries in South Brazil and the results provided by the AHP method, considering the categories of Tourqual protocol, are consistent with the opinion of specialists in wine tourism.

The model needs to be applied to other case studies to evaluate the consistency of the results and their acceptability by the tourism sector.

The model has the potential to be applied as a formal tool for evaluation of wineries, support decision-making processes in different wine tourism management structures: private wine and tourism organizations; public managers of tourism activity and managers of governance structures.

This paper presents a novel AHP-based model for evaluation of service quality in the winery’s tourism domain, an empirical application of the model for evaluation of wineries in one of the most important regions that produce grapes and wine in South America.

The maintenance of process improvement programs within organizations is not an easy task. Some processes do not maintain the results achieved with the improvement project and again present low performance shortly after completion of the project. This paper aims to identify the socio-technical causes responsible for the loss of process performance after applying improvement projects, in order to support the mitigation of the problem.

A systematic review of the literature was carried out, which allowed the selection of 28 articles. The empirical stage used the benchmarking technique, along a large Brazilian mining company with an international standard of production and quality. This organization has been using improvement projects for more than ten years.

The research complements the literature regarding the prioritization of the 26 identified failures and the identification of seven new failure factors and two resources. Findings in this study can be an inspiration for other organizations, considering the similarity of methodological aspects of improvement projects since internationally standardized methodologies such as Six Sigma, Lean, Kaizen and 5S are used.

The research presented a proposal of guidelines that corroborate to the mitigation of loss of process performance after improvement project.

This paper aims to describe the obtainment of Ti-6Al-4V parts with a hierarchical arrangement of pores by additive manufacturing, aiming at designing orthopedic implants.

The experimental methodology compares microstructural and mechanical properties of Menger pre-fractal sponges of Ti-6Al-4V alloy, manufactured by laser powder bed fusion (LPBF) and electron beam powder bed fusion (EBPBF), with three different porosity volumes. The pore arrangement followed the formation sequence of the Menger sponge, with hierarchical order from 1 to 3.

The LPBF parts presented a martensitic microstructure, while the EBPBF parts presented an α + ß microstructure, independently of its wall thickness. The LPBF parts presented higher mechanical resistance and effective stiffness than the EBPBF parts with similar porosity volume. The stiffness values of the Menger pre-fractal sponges of Ti-6Al-4V alloy, between 4 and 29 GPa, are comparable to those of the cortical bone. Furthermore, the deformation behavior presented by the Menger pre-fractal sponges of Ti-6Al-4V alloy did not follow the Gibson and Ashby model's prediction.

To the best of the authors' knowledge, this is the first study to obtain Menger pre-fractal sponges of Ti-6Al-4V alloy by LPBF and EBPBF. The deformation behavior of the obtained porous parts was contrasted with the Gibson and Ashby model's prediction.

The study aims to propose a circular economy-based reverse logistics (CERL) that emphasises the mediation effect of reverse logistics (RL) on sustainable resource commitment and financial performance.

The structural equation modelling (SEM) approach has been applied to analyse the data acquired through the survey method that included 113 vendors of automotive supplies of the 1st and 2nd levels.

The results confirm that CERL acts as an essential intervening entity between resources and financial performance. The findings of the study have provided research and development (R&D) opportunities for the industries to find alternative revenue streams and generate profit from resource investment whilst upholding environmental standards through reverse logistic practices.

Reverse logistic practices are the key components of a circular business model and a sustainable supply chain. The manufacturing companies need to explore critical enablers that can contribute to business productivity and financial growth.

The study has validated a CERL model that portrays the circular economy's resilient relationship with RL practices.