Patricia Mendes dos Santos, Marcelo Ângelo Cirillo and Elisa Reis Guimarães
Building on Guimarães et al. (2019) study and using the modeling of structural equations, the objective of this paper was to elaborate constructs whose variables would enable the…
Abstract
Purpose
Building on Guimarães et al. (2019) study and using the modeling of structural equations, the objective of this paper was to elaborate constructs whose variables would enable the characterization and distinction of individuals among these different groups of consumers and to provide insights into their transition between them.
Design/methodology/approach
The constructs were validated by the average variance extracted adaptive (AVEADP) index. The transition between consumer groups is explained and encouraged by advances in their conceptual and perceptual knowledge. Thus, regular consumers should be addressed with messages aimed primarily for the social aspect of consumption; enthusiasts, by reinforcing simple to moderate aspects commonly used as product purchase criteria and experts, attracted by the emphasis on complex criteria related to specialty coffee's conceptual and perceptual knowledge, highlighting their influence on the beverage's sensory profile.
Findings
Those results enabled a better understanding of these consumers and can guide the marketing strategies of different actors in this market.
Originality/value
Important attempts to understand and characterize Brazilian specialty coffee consumers were conducted by Guimarães et al. (2019) and Ramírez-Correa et al. (2020). However, further studies are needed to differentiate different specialty coffee consumer groups and enhance the market applicability of those studies results. In addition, despite its importance, there is a paucity of public domain studies about the national consumption of specialty coffees, being the results of this work important for the wide dissemination of such information.
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Fernando Augusto Gouvea-Reis, Danniely Carolinne Soares da Silva, Lairton Souza Borja, Patrícia de Oliveira Dias, Jadher Percio, Cassio Peterka, Janaína de Oliveira, Giselle Sodré, Claudia Mendes Feres, Wallace Dos Santos, Fábio Souza, Ana Izabel Passarella Teixeira, Daiani Cristina Cilião-Alves, Gustavo Adolfo Sierra Romero, Elza Ferreira Noronha, Julio Croda, Rodrigo Haddad, Walter Massa Ramalho, Camile de Moraes and Wildo Navegantes de Araújo
This study aims to estimate the overall SARS-CoV-2 seroprevalence and evaluate the accuracy of an antibody rapid test compared to a reference serological assay during a COVID-19…
Abstract
Purpose
This study aims to estimate the overall SARS-CoV-2 seroprevalence and evaluate the accuracy of an antibody rapid test compared to a reference serological assay during a COVID-19 outbreak in a prison complex housing over 13,000 prisoners in Brasília.
Design/methodology/approach
The authors obtained a randomized, stratified representative sample of each prison unit and conducted a repeated serosurvey among prisoners between June and July 2020, using a lateral-flow immunochromatographic assay (LFIA). Samples were also retested using a chemiluminescence enzyme immunoassay (CLIA) to compare SARS-CoV-2 seroprevalence and 21-days incidence, as well as to estimate the overall infection fatality rate (IFR) and determine the diagnostic accuracy of the LFIA test.
Findings
This study identified 485 eligible individuals and enrolled 460 participants. Baseline and 21-days follow-up seroprevalence were estimated at 52.0% (95% CI 44.9–59.0) and 56.7% (95% CI 48.2–65.3) with LFIA; and 80.7% (95% CI 74.1–87.3) and 81.1% (95% CI 74.4–87.8) with CLIA, with an overall IFR of 0.02%. There were 78.2% (95% CI 66.7–89.7) symptomatic individuals among the positive cases. Sensitivity and specificity of LFIA were estimated at 43.4% and 83.3% for IgM; 46.5% and 91.5% for IgG; and 59.1% and 77.3% for combined tests.
Originality/value
The authors found high seroprevalence of anti-SARS-CoV-2 antibodies within the prison complex. The occurrence of asymptomatic infection highlights the importance of periodic mass testing in addition to case-finding of symptomatic individuals; however, the field performance of LFIA tests should be validated. This study recommends that vaccination strategies consider the inclusion of prisoners and prison staff in priority groups.
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Barbara de Lima Voss, David Bernard Carter and Bruno Meirelles Salotti
We present a critical literature review debating Brazilian research on social and environmental accounting (SEA). The aim of this study is to understand the role of politics in…
Abstract
We present a critical literature review debating Brazilian research on social and environmental accounting (SEA). The aim of this study is to understand the role of politics in the construction of hegemonies in SEA research in Brazil. In particular, we examine the role of hegemony in relation to the co-option of SEA literature and sustainability in the Brazilian context by the logic of development for economic growth in emerging economies. The methodological approach adopts a post-structural perspective that reflects Laclau and Mouffe’s discourse theory. The study employs a hermeneutical, rhetorical approach to understand and classify 352 Brazilian research articles on SEA. We employ Brown and Fraser’s (2006) categorizations of SEA literature to help in our analysis: the business case, the stakeholder–accountability approach, and the critical case. We argue that the business case is prominent in Brazilian studies. Second-stage analysis suggests that the major themes under discussion include measurement, consulting, and descriptive approach. We argue that these themes illustrate the degree of influence of the hegemonic politics relevant to emerging economics, as these themes predominantly concern economic growth and a capitalist context. This paper discusses trends and practices in the Brazilian literature on SEA and argues that the focus means that SEA avoids critical debates of the role of capitalist logics in an emerging economy concerning sustainability. We urge the Brazilian academy to understand the implications of its reifying agenda and engage, counter-hegemonically, in a social and political agenda beyond the hegemonic support of a particular set of capitalist interests.
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Mostafa Abbasi, Rahnuma Islam Nishat, Corey Bond, John Brandon Graham-Knight, Patricia Lasserre, Yves Lucet and Homayoun Najjaran
The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation…
Abstract
Purpose
The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning artificial intelligence (AI) and machine learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.
Design/methodology/approach
In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology to analyze related papers.
Findings
In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis.
Research limitations/implications
While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024.
Originality/value
This work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools. Hence, it offers comprehensive guidelines that elucidate the alignment between ML methods and solutions to current challenges across the BPM life-cycle, including process enhancement and process improvement. Additionally, by detailing various aspects of the life-cycle phases and highlighting ML technique characteristics, this research demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.
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Ethan McGuirk and Patricia Frazer
The prevalence of mental health (MH) issues amongst post-secondary students is on the rise. This study aims to assess if a student’s mental well-being (MWB) is impacted by a range…
Abstract
Purpose
The prevalence of mental health (MH) issues amongst post-secondary students is on the rise. This study aims to assess if a student’s mental well-being (MWB) is impacted by a range of predictors such as gender, education level, mental health literacy (MHL) and the post-secondary campus climate.
Design/methodology/approach
A correlational, cross-sectional design was implemented amongst a student population (N = 100). A questionnaire was administered electronically to participants’. Levels of MWB, campus climate and MHL were evaluated alongside a number of demographics.
Findings
Campus climate was a significant predictor of student MWB. Gender differences were discovered amongst MHL levels. MHL was found to be significantly associated with the level of education.
Originality/value
This study is one of few evaluating the relationship between MWB, MHL and the post-secondary campus climate. Based on these findings, the post-secondary campus may predict student MWB, therefore can be possibly augmented to assist students. Additionally, MHL interventions should focus on education level and gender-specific cohorts to enhance student MWB.
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Bernardo Cerqueira de Lima, Renata Maria Abrantes Baracho, Thomas Mandl and Patricia Baracho Porto
Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication…
Abstract
Purpose
Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.
Design/methodology/approach
To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.
Findings
We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.
Research limitations/implications
The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.
Practical implications
With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.
Originality/value
This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.