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This article aims to identify management practices that evidence how internal control have been considered essential, from the edition of the State Constitutional Amendment no. 75 of 2012, within the public administration of the executive branch of the State of Ceará, during the period 2012–2021.

The study relates the identified management practices to COSO (The Committee of Sponsoring Organizations) methodology “Internal Environment” component categories. The research is classified as basic, exploratory and bibliographic, on the theme of internal control in scientific articles published between 2015 and 2021, and documental, carried out through official documents, including the 27 Brazilian constitutions.

Existence of management practices that corroborate the essentiality of internal control in Ceará.

The study is limited to evidencing the control practices implemented in Ceará, not evaluating them as to their quality.

Contributions on control on constitutional-legal bases for other Brazilian Federation States.

Possibility of introducing the research theme into various branches of scientific knowledge, such as political science and contributing to public organizations to implement policies with the proper application of resources for the benefit of society.

The originality of the research is in demonstrating the essentiality of internal control in the State of Ceará, from the edition of management acts performed by the executive branch, based on Constitutional Amendment 75 of 2012, which did not become a dead letter of the law, enabling other states of the federation to do the same.

investigate and analyze the aspects of legitimation, theorization and trends for the evolution of research in information technology governance (ITG) in Brazil, according to researchers familiar with the matter.

By means of a qualitative and quantitative research of exploratory-descriptive approach, the Delphi method was applied using a questionnaire supported by content analysis.

ITG is an increasingly interdisciplinary research field, with significant help from other fields of knowledge, such as administration, computer science and engineering. The main means of ITG publication are periodicals (MISQ, JMIS, JISTEM RESI), scientific events (AMCIS, ECIS, HICSS, EnANPAD, CONTECSI) and researchers, such as Peter Weill and Edimara Mezzomo Luciano. Best practice models are the most significant theoretical frameworks, and the main trend of research are on emerging technologies such as cloud computing and Internet of things (IoT) in the context of ITG.

To the unavailability of some researchers to participate in the second phase of the Delphi research performed, as well as the non-completion of a third Delphi round. Likewise, the “Block B (open answer questions)” it was not contemplated in the second phase for a new collection of answers, which could partially change the results presented here.

The results show important insights for ITG researchers that can allow new researches about its applications, jointly reflecting on relevant aspects for the advancement of this research field.

There are several research contributions to broaden the discussion and the evolution of this new scientific field in Brazil and that can be grouped for each set of stakeholders: academia and related researchers; the practicing community of business managers and private and public organizations; the academic legitimizing bodies; the non-academic legitimating bodies and researchers from other areas of knowledge.

ITG is a concept that emerged as part of corporate governance (CG), which has evolved as an emerging theme and is expanding in the international academic arena. However, the current stage of legitimation, theorization and trends of ITG in the Brazilian researches are lacked greater understanding, in order to provide better targeting for new researches.

Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).

The ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.

The analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.

The analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.

The novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

This research sought to propose a theoretical model that analyzes the factors associated with unlearning (individual and organizational) and contributes to generating and realizing ideas among young people at the beginning of their careers based on the predominant type of structure.

The study had a sample (n = 971) and used the multivariate data analysis partial least squares - Structural Equation Modeling (PLS-SEM regular) and multigroup analysis (PLS-MGA) to identify significant differences between the estimates of the specific parameters of each group (a- Organic/b- Mechanistic).

All the direct relationships and formulated mediations were found to be supported, except for H6 (ET→EO) within the group that had a primarily mechanistic organizational structure. Thus, the more turbulent the environmental, the more initiative-taking, innovative and risk-taking a company tends to be. However, it remains to be seen whether the organizational structure plays a role in facilitating or hindering this relationship. H1 (IG→IR) indicates that predominantly organic organizations have a stronger and more consistent relationship with the knowledge developed through individual and organizational unlearning process. This knowledge contributes to the idea-generation process and ultimately leads to realizing those ideas.

The article contributes to literature by proposing an original and integrated theoretical model incorporating individual and organizational approaches to unlearning to understand the effect on idea generation and realization.

This study aims to analyze communication from the perspective of social marketing, positive emotions, and the topics chosen by Spanish tourist destinations to show their destination image. Additionally, this research shows a message classification model, based on the aforementioned characteristics, that has generated a greater impact, offering clarity to tourism managers on the type of content they should publish to achieve greater visibility.

The methodology used in this work combines content analysis and data mining techniques. The classification tree using the chi-square automatic interaction detector (CHAID) algorithm was selected to determine predictors of like behaviour.

The results show that the predictor variables have been emotions, social marketing and topics. Also, the characteristics of the messages most likely to have a high impact are those related to emotions of joy or happiness, their purpose is behavioural, and they talk about rural, cultural issues, special dates, getaways, or highlights of a town or city for something specific.

This study is the first to analyze the content of the tweets shared by destination tourism managers from a social marketing, positive emotions, and sustainability perspective, determining the possible predictors of likes on Twitter. The authors contribute to the literature by deepening the understanding of how social marketing and the positive emotions promoted drive a more significant impact in tourism communication campaigns on social media. The authors provide destination managers with a way better to understand the variables relevant to users in tourism content.

This paper aims to analyze how decision support systems manage Big data to obtain value.

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

This study aims to measure the effects of moderation by age and income in mobile payment systems' intended use as predictors of performance expectation, effort expectation, social influence, risk and perceived costs.

This study is based on a survey that generated a sample of 1,742 Brazilian users that responded to the measurement scale. The research data were analyzed using the partial least squares structural equation model.

All proposed latent variables were significant, with income positively moderating the performance expectation and negatively moderating the perceived cost and perceived risk. In addition, age positively moderates performance expectation and negatively moderates cost perception.

The findings evolved previous literature by understanding moderating effects that make it possible for companies operating in mobile payments to generate segmented communication and engagement plans for users of different income and age brackets.

The purpose of this paper is to present a conceptual model that defines the essential components shaping the new Digital Supply Chains (DSCs) through the implementation and acceleration of Industry 4.0.

The scope of the present work exposes a conceptual approach and review of the key literature from 1989 to 2019, concerning the evolution and transformation of the actors and constructs in logistics and Supply Chain Management (SCM) by means of examining different conceptual models and a state-of-the-art review of Industry 4.0’s concepts and elements, with a focus on digitization in supply chain (SC) processes. A detailed study of the constructs and components of SCM, as defined by their authors, resulted in the development of a referential and systematic model that fuses the inherent concepts and roles of SCM, with the new technological trends directed toward digitization, automation, and the increasing use of information and communication technologies across logistics global value chains.

Having achieved an exploration of the different conceptual frameworks, there is no compelling evidence of the existence of a conceptual SCM that incorporates the basic theoretical constructs and the new roles and elements of Industry 4.0. Therefore, the main components of Industry 4.0 and their impact on DSC Management are described, driving the proposal for a new conceptual model which addresses and accelerates a vision of the future of the interconnectivity between different DSCs, grouped in clusters in order to add value, through new forms of cooperation and digital integration.

This research explores the gap in the current SCM models leading into Industry 4.0. The proposed model provides a novel and comprehensive overview of the new concepts and components driving the nascent and current DSCs. This conceptual framework will further aid researchers in the exploration of knowledge regarding the variables and components presented, as well as the verification of the newly revealed roles and constructs to understand the new forms of cooperation and implementation of Industry 4.0 in digitalized SCs.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

This study addresses the COVID-19 infection and its relationship with the city’s constructive intensity, commuting time to work and labor market dynamics during the lockdown period.

Microdata from formal workers in Recife was used to adjust a probability model for disease contraction.

The authors' results indicate that greater distance to employment increases the probability of infection. The same applies to constructive intensity, suggesting that residences in denser areas, such as apartments in buildings, condominiums and informal settlements, elevate the chances of contracting the disease. It is also observed that formal workers with completed higher education have lower infection risks, while healthcare professionals on the frontlines of combating the disease face higher risks than others. The lockdown effectively reduced contagion by limiting people’s mobility during the specified period.

The research shows important causal relationships, making it possible to think about public policies for the health of individuals both when commuting to work and in living conditions, aiming to control contagion by COVID-19.

The lockdown effectively reduced contagion by limiting people’s mobility during the specified period.

It is also observed that formal workers with completed higher education have lower infection risks, while healthcare professionals on the frontlines of combating the disease face higher risks than others.

The authors identified positive and significant relationships between these urban characteristics and increased contagion, controlling for neighborhood, individual characteristics, comorbidities, occupations and economic activities.