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This study aims to develop a risk prioritization process using failure mode and effect analysis (FMEA) in association with composition of probabilistic preferences (CPP) and weighting the risk analysis criteria. It seeks to develop decision-making considering the fast response necessary to achieve project objectives in complex scenarios, such as the pandemic of COrona VIrus Disease 19 (COVID-19).

After identifying the risks, the prioritization process was applied to a project in the oil and gas area, in which a focus group assessed these risks. This evaluation took place employing traditional FMEA, FMEA with CPP by axes considering four points of view and FMEA with CPP by weighted sum with the use of a multicriteria method to weight the criteria. These approaches were compared to understand their differences and benefits, with a flow chart being developed, consolidating the procedure.

The methodologies that showed the greatest benefits were FMEA with CPP by axes PO (progressive-optimistic) and by weighted sum. Essentially, this was mainly related to the interrelationship between risks and to the importance of prioritization.

This procedure can consider company's views on what is critical and the interrelationship between risks. It provides a clear segmentation of what should and should not be prioritized. It was also developed in a practical case, showing a possible alternative to support fast responses in decision-making.

The purpose of this paper is to analyze the innovation process of organizations representing the main sectors of Brazilian economic activity.

The literature review focuses on analyzing the innovation process characteristics regarding the innovation types. The authors carried out interviews with executives and managers in charge of innovation at the leading large companies in the respective sectors analyzed. The data analysis of this qualitative research was structured in three steps. The first step is the analysis of data collected for encoding, the second step, the summarization of the common points presented by the companies in each sector and, finally, the interpretation of these data, aided by triangulation from secondary data that support the analysis of the collected primary data.

The main contribution of this study is to characterize the innovation process of organizations representing the main sectors of the Brazilian economy, with a classification regarding the sectoral innovation standard.

The authors’ intent is that the paper can contribute with a comparative analysis among companies of the same sector and, subsequently, among companies of the different surveyed sectors. Thus, the characterization aims to present the companies’ innovation process and the comparative analysis aims to verify the innovation sectoral patterns. In addition, as implications for management practice, some strategies for better knowledge management in the organization are suggested for each type of innovation.

The main theoretical contribution focuses on the development of a conceptual model that structures the analyzed variables of the constructs “innovation process” and “innovation sectoral patterns”, allowing not only the characterization but also the comparative analysis of the representative organizations present in the sample.

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.

This paper aims to develop a cross-border regional brand management model to help enhance cooperation for developing such localities. It analyzed its applicability in the Brazil–Argentina and Italy–Austria cross-border regions comprising several cities.

A systematic literature review was conducted to obtain a theoretical basis and select elements for creating the cross-border regional brand management (CRBM) model. To apply the model, in-depth interviews were conducted with 19 specialists of different nationalities and distinct expertise on cross-border governance and regional branding. In addition, to validate the proposed model, a focus group was carried out, and specialists were consulted using forms, providing 22 additional opinions.

The results show good managerial practices and gaps that must be overcome to create and manage the brands from the two regions analyzed.

The theoretical contributions consist in extending the literature in place branding by presenting the first CRBM model and the conceptual explanation of each of the model's elements.

The study's practical implications occur through the suggestion of good management practices for the studied localities arising from the applicability of the CRBM model. Furthermore, it is expected that the model developed can be applied in other locations, bringing practical contributions to the management and creation of cross-border regional brands in other countries.

This study presents the first CRBM model and its applicability to two cross-border regions.

Rainfall simulators are used on experimental hydrology, in areas such as, e.g., urban drainage and soil erosion, with important timesaving when compared to real scale hydrological monitoring. The purpose of this paper is to contribute to increase the quality of rainfall simulation, namely, for its use with scaled physical models.

Two pressurized rainfall simulators are considered. M1 uses three HH-W 1/4 FullJet nozzles under an operating pressure of 166.76 kPa and was tested over a 4.00 m length by 2.00 m width V-shaped surface. M2 was prepared to produce artificial rainfall over an area of 10.00 m length by 10.00 m width. The spatial distribution of rainfall produced from a single nozzle was characterized in order to theoretically find the best positioning for nozzles to cover the full 100 m² area with the best possible rainfall uniformity.

Experiments with M1 led to an average rainfall intensity of 76.77-82.25 mm h⁻¹ with a 24.88 per cent variation coefficient and a Christiansen Uniformity Coefficient (CUC) of 78.86 per cent. The best result with M2 was an average rainfall intensity of 75.12-76.83 mm h⁻¹ with a 21.23 per cent variation coefficient and a CUC of 83.05 per cent.

This study contributes to increase the quality of artificial rainfall produced by pressurized rainfall simulators.

M2 is the largest rainfall simulator known by the authors worldwide. Its use on rainfall-runoff studies (e.g. urban areas, erosion, pollutant transport) will allow for a better understanding of complex surface hydrology processes.

This study aims to broaden the understanding of the additive manufacturing (AM) body of knowledge, presenting a model better suited to the current level of technological development that supports the decision to implement AM in industries, based on the experience of companies in the industry of orthopedic medical implants.

Based on the design-science research, the model for the decision to adopt the AM was designed and submitted to experts from the industry of orthopedic implants in Brazil for refinement. For the empirical test of the final model, interviews were used in a company that was considering implementing AM and in another that was not, to evaluate the model.

The model considers seven dimensions for decision analysis of AM implementation: legal constraints, financial, technological, operational, organizational, supply chain and external factors, being subdivided into 42 criteria that play a relevant role in the implementation decision. The analysis factor of each dimension and criteria are also presented.

The model seeks to be as complete as possible and can be used by various industrial productive sectors, incorporating the analysis of the requirements of health regulatory agencies, suitable for the analysis of the decision to implement AM for the manufacturing of medical implants, not found in other models.

The purpose of this paper was to present the stages of the Brazilian startups' organizational life cycle (OLC), identifying and describing the main factors related to the entrepreneurial and organizational perspectives, as well as the factors related to external relationship agents and the associated risks at each OLC stage. In addition, the variables that comprised the three perspectives above were used for a descriptive reading of the evolutionary process from the perspective of intellectual capital during the stages.

The type of research used was quantitative with a descriptive character. For data collection, the authors have used the survey method and two complementary questionnaires were used as research tools. The measurement scale used in these instruments was the 11-point Likert scale. In total, 100 Brazilian startups registered in the STARTUPBASE and residents in São Paulo comprised the sample of this research. For the data processing, it was used the exploratory factor analysis techniques, to identify the analysis factors, and the cluster analysis, to identify the OLC stages.

Based on the results obtained, four stages were identified and described to build the OLC model of this research: Stage I – conception and development; Stage II – organization and traction; Stage III – growth and scale; and Stage IV – consolidation and transition. In addition, the authors described the main lines of evidence found at each stage.

This research contributes to academic studies of the OLC in startups and the evolution of risks that originate from the relationship between external agents in the business environment and startups. Thus, a management map is built, which helps entrepreneurs and managers construct these businesses since such a management map allows identifying the risks and challenges that a startup needs to overcome to grow and develop.

The originality of this research lies in the adaptation of the OLC approach, which is widely used in studies analyzing the growth and development of mature organizations. The authors used this adaptation to analyze the growth and development of startups in Brazil. In addition, the identification and analysis of external agents that make up the business environment, as well as the analysis of the risks, originated from the relationships between the startup and these agents, brought original and essential results and discussions, both for OLC studies and for risk analysis studies in startups.

This paper aims to propose an approach based upon the principal component analysis (PCA) to define a contribution rate for each variable and then select the main variables as inputs to a neural network for energy load forecasting in the region southeastern Brazil.

The proposed approach defines a contribution rate of each variable as a weighted sum of the inner product between the variable and each principal component. So, the contribution rate is used for selecting the most important features of 27 variables and 6,815 electricity data for a multilayer perceptron network backpropagation prediction model. Several tests, starting from the most significant variable as input, and adding the next most significant variable and so on, are accomplished to predict energy load (GWh). The Kaiser–Meyer–Olkin and Bartlett sphericity tests were used to verify the overall consistency of the data for factor analysis.

Although energy load forecasting is an area for which databases with tens or hundreds of variables are available, the approach could select only six variables that contribute more than 85% for the model. While the contribution rates of the variables of the plants, plus energy exchange added, have only 14.14% of contribution, the variable the stored energy has a contribution rate of 26.31% being fundamental for the prediction accuracy.

Besides improving the forecasting accuracy and providing a faster predictor, the proposed PCA-based approach for calculating the contribution rate of input variables providing a better understanding of the underlying process that generated the data, which is fundamental to the Brazilian reality due to the accentuated climatic and economic variations.