This study aims to investigate the determinants of Portuguese SMEs capital structure and to examine the effects of the 2008 financial crisis on Portuguese SMEs capital structure. The sample used considers the period 2007-2010, resulting in 12,857 Portuguese SMEs. Results suggest that liquidity, asset structure and profitability are the most important determinants explaining the capital structure of Portuguese SMEs. We report a downward tendency on companies' debt ratios levels during the financial crisis.

Public scrutiny and the need for funds in a more competitive environment are pressuring nonprofits to be more consciously aware of their reputation. This study used automated analysis with text mining and topic modeling of 177 articles directly linked to nonprofits' reputation and published up to 2016. After identifying the most salient topics and conducting an in-depth, critical review of the most significant articles within each topic, four theoretical and managerial implications were identified. First, managers need to develop skills to deal with risk, the Internet, and social networks. Second, risk management is an emergent, still tentative, but important topic waiting for more contributions. Third, researchers can apply lexicons developed and validated by experts to uncover knowledge relevant to the entire nonprofit sector's organizations. Last, the trends and topics highlighted can help scholars and practitioners make better decisions in research or responses to management challenges.

The topic of donations is one of high relevance and has been widely covered in contemporary marketing literature. It is a topic of interest to both theoreticians and practitioners alike, particularly due to its implicit links to fundraising activities and research. The reality of what makes an individual donor ultimately part with his money and give it away to a nonprofit organization is a hot contemporary topic. This study looks into the role of religiosity as a predictor of donations practices. Also volunteerism and compassion, two acts of pro-social behaviour are analysed as predictors of donations practices. Using data collected from a survey of 612 charity donors in Portugal, the results show unequivocally that religiosity does influence donations practices, and so being a predictor of donations practices. Moreover, pro-social behaviour is a predictor of donations practices when in the case of volunteerism, but not in the form of compassion.• The findings are particularly useful for nonprofit organizations that want to attract and retain individual charitable donors and may also help to increase donation regularity, to obtain higher amounts, and donations both to religious and to secular organizations. Finally, it can be stated that the understanding of religiosity sheds light on knowledge about donations practices, and that this study also makes an important contribution to academia, as it is the first study conducted in Portugal that assesses the drivers of donations practices.

International Public Sector Accounting Standards (IPSASs) are a good reference for a harmonized microaccounting system allowing more transparency and quality in public sector accounting across EU member-States. However, questions remain concerning IPSASs contribution to the convergence between Governmental Accounting (GA) and the National Accounts (NA). This article assess how the proximity to an IPSAS-based accounting system in GA has impact on the diversity and materiality of GA-NA budgetary deficit/surplus adjustments, hence analyzing whether IPSASs might contribute to GA-NA reconciliation. Main findings show that IPSASs do not make considerable difference in terms of GA-NA adjustments, so IPSASs-based EPSASs will hardly contribute to approaching GA-NA.

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.