Purpose: The purpose of this paper is to study the existence of psychological barriers in cryptocurrencies. Design/methodology/approach: To detect psychological barriers, the authors perform a uniformity test, a barrier hump test, a barrier proximity test and conditional effects test to a sample comprised by the daily closing quotes of six of the most liquid cryptocurrencies. Findings: The results evidence the existence of psychological barriers in four of the cryptocurrencies under scrutiny, namely, Bitcoin, Dash, NEM and Ripple. Practical implications: The fact that the cryptocurrency market has a high share of unexperienced investors and presents several cases of psychological barriers is consistent with the hypothesis that that class of investors is particularly prone to the behavioral biases which cause psychological barriers. Originality/value: This paper studies, for the first time, the existence of psychological barriers in the market of cryptocurrencies.

Gender diversity in management has become of increased interest to academics and researchers in the last decades since women and men have different personal and psychological characteristics that affect their decision-making process in management actions and leadership. A female presence has been under-represented on companies’ boards of directors, which affects management decisions and leadership style. This article aims to contribute to the analysis of gender diversity and leadership state of knowledge, conducting a bibliometric review of the existing literature. In this context, we analyze the evolutional research studies published in the Scopus digital library, considering a period of five years, from 2017 to 2021. We focus our analysis on the top 24 cited articles after a wider review of articles published relating to the explored topic (402 articles). Considering the bibliometric analysis done, we then present scrutiny of the state of knowledge on this subject, a summary of the existing gaps, and suggestions for future research.

Purpose: This paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).Design/methodology/approach: The authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.Findings: The results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.Practical implications: Forecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.Originality/value: To the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.