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The coronavirus disease (COVID-19) pandemic generated a worldwide financial crisis by impacting several links of the supply chain, however companies can take advantage by quantitatively measuring the disruptive impacts.

This study sought to develop the failure mode and effect analysis and supply chain resilience (FMEA-SCR), a hybrid tool developed using a potential failure mode and effect analysis (FMEA) applied to supply chain resilience (SCR) and taking into account the capability factors and business processes.

In order to validate, the proposed model was applied into two different organizational study cases: an university and a cooperative managing urban solid wastes with recyclable potential (MSWRP). Through the procedures described here any organization can understand and assess in a simplified way the impacts over their supply chain generated by such a crisis.

This study synthesizes three different procedures into a single method called FMEA-SCR, allowing organizations to understand and assess in a simplified way, the impacts over their supply chain generated by COVID-19. To this end, it brought together the studies developed by Rajesh and Ravi (2015) and Curkovic et al. (2015), on possible causes of disruptions in SC, the capability factors of Pettit et al. (2010) used by organizations to mitigate the effects of disruptions, besides Lambert's and Croxton (2005) business processes, thus weaving a method that allows organizations to visualize, analyze and classify the pandemic impacts over their supply chain.

The purpose of this paper is to evaluate the efficiency in financial intermediation and the cost efficiency in banking service of credit unions in Brazil, based on essentially accounting variables, and to analyze the temporal evolution of the efficiency of these cooperatives.

With a sample of 315 cooperatives over the period from 2007 to 2014, this research uses a two-stage process: application of regression models with panel data to verify which variables are related to the defined outputs, with the reduction of 31 variables to 8 variables in both models; and application of the data envelopment analysis method to obtain an analysis of credit unions’ efficiency.

The results demonstrate a high level of efficiency in financial intermediation, with low variation over time, associated with a low efficiency in the banking service, in which few cooperatives have remained efficient over time. In addition, the cooperatives with highest efficiency in financial intermediation were also the most efficient in providing services.

This research has some limitations about the capacity of the proxies used to capture the real effect of the variables and assumptions of economic relations resulting in restrictions to generalize the results.

Cooperatives are usually analyzed under just one dimension. By separating the analysis into financial intermediation and banking services, cooperatives that are more efficient in each dimension can be identified, in addition to analyzing the evolution over time. The authors found that efficiency tends to be lower in banking services, and few cooperatives remain at the highest level of efficiency over time in both models.

Credit unions provide an important service in the banking and credit market. Therefore, understanding its operation and the characteristics that influence its efficiency allows a better management of the cooperatives themselves and a greater understanding of this important segment of the financial market.

The need to improve the efficiency and quality of public services has increased the interest in innovation. This study seeks to understand the relationship between the Public Service Motivation (PSM) index and the profile of strategic-level public servants who are considered innovative.

Questionnaires were applied to the group of government managers who registered their projects in an innovation competition. Data were analyzed using t-test, multiple correspondence analysis (MCA) and hierarchical cluster analysis (HCA).

This paper concluded that the PSM level of these innovative public servants is significantly greater than public servants overall. In terms of the profile of these innovative government managers, it was found that the majority were female, with a high level of education and a background in Information Technology.

The discovery that innovative government managers have a significantly higher PSM (p < 0.05) than public servants overall may be relevant, because it confirms a statistical tendency that it would be advantageous for policy-makers to invest in actions that increase public servant PSM since these servants with high PSM are more innovative.

This article has sought to fill a gap in studies that associate the level of PSM with the innovation practices in the public sector, as well as verify the profile of public servants with high levels of motivation in public service (PSM).

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.

The purpose of this study is to identify innovation models in the judiciary according to the current integrated theoretical approach for innovation in services.

This study uses a quantitative approach. The authors collected the data through a questionnaire sent to labor court public servants and judges in a Regional Labor Court in the Midwestern region of Brazil. They performed a principal component analysis to identify the factors to map the innovation models present in the court.

Two factors were obtained from the results, which describe innovations in processes and services in the court studied. In terms of the examples of innovations cited by the respondents, one may note that those related to information and communications technology are the most remembered, especially the introduction of the electronic lawsuit.

The results can contribute toward a deeper understanding of which vectors of service innovation are affected as well as the nature of the court’s underlying structure. Also, the research instrument used allows the identification and analysis of the innovation model for services and thus contributes to its validation.

Research dealing with earnings management in the public-sector context is expanding. This paper aims to review the existing literature to understand how research is developing and points out gaps deserving further investigation.

This study uses the structured literature methodology to investigate the state-of-the-art and future directions of the literature on earnings management in the public sector. In total, 78 articles were explored.

The critical analysis of the literature shows that different but related streams of literature are emerging, focused on both a macro- and a micro-level perspective (mainly local governments and state-owned enterprises).

This study is the first that offers a comprehensive review of the literature on the emerging topic of earnings management in the public-sector context. The structured literature review enables the identification of future directions for the literature in this field.

This study aims to examine the relationship between female directors and bank risk. In particular, whether such a relationship varies across sound or unsound banks and with or without a critical mass of female directors is tested.

Using a sample of 215 listed banks from 40 countries over the period 2008–2016, this study carries out panel data analyses and tests all the model specifications on four different measures of risk (common equity ratio, leverage, NPLs ratio and price volatility).

The findings show that increasing the number of female directors does not reduce bank risk when banks are unsound. When banks are sound, female directors have a significant and positive role in reducing risk, only until reaching a critical mass of women.

This study provides useful corporate governance indications for policymakers and practitioners. Advantages of gender diversity on boards are recognized especially in sound banks, but increasing the number of women directors beyond the critical mass may not lead to lower risk. In fact, ethical or legal pressures aimed at increasing gender diversity on boards (i.e. soft or hard gender quotas) may cause undesired effects on bank risk, especially if female directors are not chosen on merit and skills. Moreover, gender-balanced boards, namely, with a “dual critical mass,” seem to assure more effective decision-making processes.

This study provides empirical evidence on female board members and risk minimization, differentiating between sound or unsound banks. Furthermore, this study contributes to the literature on the critical mass of women on the board of directors by testing this theory for these two categories of banks.