BRAZIL: Batista arrest may buy Temer time
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DOI: 10.1108/OXAN-ES224351
ISSN: 2633-304X
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Lorena Mota, Maureen Mayhew, Karen J. Grant, Ricardo Batista and Kevin Pottie
International migrants frequently struggle to obtain access to local primary care practices. The purpose of this paper is to explore factors associated with rejecting and…
Abstract
Purpose
International migrants frequently struggle to obtain access to local primary care practices. The purpose of this paper is to explore factors associated with rejecting and accepting migrant patients into Canadian primary care practices.
Design/methodology/approach
Mixed methods study. Using a modified Delphi consensus approach among a network of experts on migrant health, the authors identified and prioritized factors related to rejecting and accepting migrants into primary care practices. From ten semi-structured interviews with the less-migrant-care experienced practitioners, the authors used qualitative description to further examine nuances of these factors.
Findings
Consensus was reached on practitioner-level factors associated with a reluctance of practitioners to accept migrants − communication challenges, high-hassle factor, limited availability of clinicians, fear of financial loss, lack of awareness of migrant groups, and limited migrant health knowledge – and on factors associated with accepting migrants − feeling useful, migrant health education, third party support, learning about other cultures, experience working overseas, and enjoying the challenge of treating diseases from around the world. Interviews supported use of interpreters, community resources, alternative payment methods, and migrant health education as strategies to overcome the identified challenges.
Research limitations/implications
This Delphi network represented the views of practitioners who had substantive experience in providing care for migrants. Interviews with less-experienced practitioners were used to mitigate this bias.
Originality/value
This study identifies the facilitators and challenges of migrants’ access to primary care from the perspective of primary care practitioners, work that complements research from patients’ perspectives. Strategies to address these findings are discussed.
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Sérgio Moro, Paulo Rita, Cristina Oliveira, Fernando Batista and Ricardo Ribeiro
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Mariana Cavique, Antónia Correia, Ricardo Ribeiro and Fernando Batista
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Luciana Teixeira Batista, José Ricardo Queiroz Franco, Ricardo Hall Fakury, Marcelo Franco Porto, Lucas Vinicius Ribeiro Alves and Gabriel Santos Kohlmann
The objective of this research is to develop an solution to water management at the scale of buildings, through the technological resources. Automating analysis using 3D models…
Abstract
Purpose
The objective of this research is to develop an solution to water management at the scale of buildings, through the technological resources. Automating analysis using 3D models helps increase efficiency in buildings during the operational phase, consequently promotes sustainability.
Design/methodology/approach
This study presents a methodology based on Design Science Research to automate water management at building scale integrating BIM-IoT-FM. Data from smart meters (IoT) and the BIM model were integrated to be applied in facilities management (FM) to improve performance of the building. The methodology was implemented in a prototype for the web, called AquaBIM, which captures, manages and analyzes the information.
Findings
The application of AquaBIM allowed the theoretical evaluation and practical validation of water management methodology. By BIM–IoT integration, the consumption parameters and ranges for 17 categories of activities were determined to contribute to fulfill the research gap for the commercial buildings. This criterion and other requirements are requirements met in order to obtain the AQUA-HQE environmental sustainability certification.
Practical implications
Traditionally, water management in buildings is based on scarce data. The practical application of digital technologies improves decision-making. Moreover, the creation of consumption indicators for commercial buildings contributes to the discussion in the field of knowledge.
Originality/value
This article emphasizes the investigation of the efficiency of use in commercial buildings using operational data and the use of sustainable consumption indicators to manage water consumption.
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Nuno Antonio, Ana Maria de Almeida, Luís Nunes, Fernando Batista and Ricardo Ribeiro
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Ricardo Manuel Da Costa Melo, Eunice Cristina Ribeiro Lopes, José Luis Coelho Vilas Boas, Lúcia Batista Santos, Sandra Cristina Ferreira Amaro, João Miguel Almeida Ventura-Silva and Isabel de Jesus Oliveira
The impact of dependence on self-care on people’s lives is very significant, with consequences for the person and their caregivers. The purpose of this study is to map the…
Abstract
Purpose
The impact of dependence on self-care on people’s lives is very significant, with consequences for the person and their caregivers. The purpose of this study is to map the evidence on the factors that influence the empowerment of the person dependent on self-care on returning home.
Design/methodology/approach
Scoping review according to the criteria proposed by the Joanna Briggs Institute: population (people with a dependence on self-care), concept (factors that influence training) and context (return home after hospitalization in a medical-surgical context). The research was carried out from March 1 to April 30, 2022, in the databases CINAHL and MEDLINE (via EBSCO), Scielo, LILACS, Cuiden and MedicLatina; Gray literature searched RCAAP, DART-Europe and OpenGrey. Studies published in Portuguese, Spanish and English were included, with no time limit.
Findings
One hundred and eighty-one articles were obtained, which, after analysis according to the criteria, resulted in seven studies included for review, ranging from 2007 to 2021, with a level of evidence between 2. c and 4. a (according to Joanna Briggs Institute), and two thematic areas/four categories emerging.
Research limitations/implications
The need for information and training, the relationship and proximity with the health-care team, the design of nursing care targeted at the person’s level of dependence, education, gender, type of surgical intervention and postoperative period, physical space and lack of privacy and audiovisual media.
Originality/value
The perception of these factors proves to be important in the person’s training process, with the nurse’s role being highlighted due to their emphasis on the transition home.
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Devisson Mesquita dos Santos, Fernanda Leandra Leal Lopes, André Cristiano Silva Melo, Denilson Ricardo de Lucena Nunes, Izabela Simon Rampasso and Vitor William Batista Martins
This paper is dedicated to elaborating, proposing and validating an action plan to enhance the mitigation of risks generated by the COVID-19 pandemic in the electric sector supply…
Abstract
Purpose
This paper is dedicated to elaborating, proposing and validating an action plan to enhance the mitigation of risks generated by the COVID-19 pandemic in the electric sector supply chain, aiming to promote a more resilient supply chain.
Design/methodology/approach
For this, a systematic review of the literature was carried out to prepare an action plan that was validated by a group of experts, through the Delphi methodology.
Findings
As a result, an action plan was obtained, with 18 actions subdivided into 13 resilience elements and related to 20 main risks arising from the pandemic. The actions oriented to the development of relationships among supply chain members, promotion of a culture oriented to learning and problem solving, contingency plan, safety stock and risk management were pointed as those capable of generating resilience in the chain analyzed in the moment of crisis.
Originality/value
The results achieved can contribute to the expansion of debates in the area of resilient supply chain management, as well as contribute to supply chain managers in their elaboration and definition of actions that aim to make the supply chain more resilient. It is noteworthy that no similar study was found in the literature considering the specificities of supply chain management in the Brazilian Amazon region.