(1) Background: The COVID-19 pandemic has highlighted attention on the importance of certain variables in predicting job performance. Among these, mental health is one of the main variables affected by this pandemic. It can have an important mediating role in predicting job performance by individual, occupational, and organizational variables, especially in the nursing community. However, there is little information about its mediation role among the predictors of job performance. This cross-sectional study aimed to examine the role of mental health as a mediating factor in the influence of self-compassion and work engagement on ICU nurses’ job performance during the COVID-19 pandemic, as well as the moderating effect of gender on all model relations. A survey of 424 ICU nurses (men 193 and women 231) was undertaken in three Coronavirus hospitals in Isfahan, Iran. (2) Method: Questionnaires were distributed and collected among the statistical sample, and the data from the questionnaires were analyzed using AMOS24 software (version 24). The research model was evaluated in two stages (the main model and the two sub-models in two gender groups). (3) Result: The analysis revealed that work engagement (β = 0.42, p < 0.001), mental health (β = 0.54, p < 0.001) and job performance (β = 0.51, p < 0.001) were discovered to be positively related to self-compassion. Work engagement is positively associated with mental health (β = 0.16, p < 0.01) and job performance (β = 0.21, p < 0.001), and mental health is linked positively to job performance (β = 0.23, p < 0.001). Furthermore, the effects of self-compassion and work engagement on job performance are mediated by mental health. According to the findings, gender moderates the link between self-compassion and work engagement, work engagement and job performance, and self-compassion and job performance. (4) Conclusion: Mental health has a mediating role in the effect of self-compassion and work engagement on ICU nurses’ job performance. Gender also acted as a moderator in some relationships. Males are dominant in all of these relationships as compared to females.

For many years, entrepreneurs were considered the change agents of their societies. They use their initiative and innovative minds to solve problems and create value. In the aftermath of the digital transformation era, a new group of entrepreneurs have emerged who are called transformational entrepreneurs. They use various digital platforms to create value. Surprisingly, despite their importance, they have not been sufficiently investigated. Therefore, this research scrutinizes the elements affecting transformational entrepreneurship in digital platforms. To do so, the authors have considered a two-phase method. First, interpretive structural modeling (ISM) and Matrices d’Impacts Croises Multiplication Appliqué a Un Classement (MICMAC) are used to suggest a model. ISM is a qualitative method to reach a visualized hierarchical structure. Then, four unsupervised machine learning algorithms are used to ensure the accuracy of the proposed model. The findings reveal that transformational leadership could mediate the relationship between the entrepreneurial mindset and thinking and digital transformation, interdisciplinary approaches, value creation logic, and technology diffusion. The GMM in the full type, however, has the best accuracy among the various covariance types, with an accuracy of 0.895. From the practical point of view, this paper provides important insights for practitioners, entrepreneurs, and public actors to help them develop transformational entrepreneurship skills. The results could also serve as a guideline for companies regarding how to manage the consequences of a crisis such as a pandemic. The findings also provide significant insight for higher education policymakers.

The present study aimed to investigate the effect of online social networks on women’s entrepreneurship in Hungary and Iran in a comparative study. The statistical population of the present study consists of women entrepreneurs active in the field of online businesses in Iran and Hungary. The questionnaire was distributed via online links on social networks (on the online business website, Twitter, Facebook, and Instagram), and respondents were asked to answer questions if they were women entrepreneurs. ANOVA test was used to examine the variable of online social networks on different platforms in Iran and Hungary. The results indicate that Facebook media in Hungary in women entrepreneurship is significantly different from other online social networks. Based on the data collected, more than 95% of women entrepreneurs in Hungary use Facebook. Conversely, in Iran, Instagram has a significant difference in women's entrepreneurship from other online social networks and is the most popular media in women’s entrepreneurship. So that more than 92% of female respondents in Iran have a channel related to their online business in Instagram media. These important findings emphasize the importance and impact of social networks as entrepreneurial channels according to the atmosphere of each country.

In order to achieve a specific result, a firm’s problem-solving activities can be thought of as a process that combines physical and cognitive actions. Its internal organization determines how information inputs are distributed among different task units and, as a result, how the cognitive workload is distributed. We tested a case study related to Iranian small and medium enterprises (SMEs). We used NCA analysis as a creative and state-of-the-art method with the help of R software to evaluate data. According to the findings, six prerequisites must be met in order to achieve a 50% level of efficient performance: innovation at a minimum of 22.7%, CSR at a minimum of 30.4%, IT investment at a minimum of 56.7%, SMM at a minimum of 38.3%, product differentiation at a minimum of 11.7%, and CRM at a minimum of 38.3%.

Liquidity is the ease of converting an asset (physical/digital) into cash or another asset without loss and is shown by the relationship between the time scale and the price scale of an investment. This article examines the illiquidity of Bitcoin (BTC). Bitcoin hash rate information was collected at three different time intervals; parallel to these data, textual information related to these intervals was collected from Twitter for each day. Due to the regression nature of illiquidity prediction, approaches based on recurrent networks were suggested. Seven approaches: ANN, SVM, SANN, LSTM, Simple RNN, GRU, and IndRNN, were tested on these data. To evaluate these approaches, three evaluation methods were used: random split (paper), random split (run) and linear split (run). The research results indicate that the IndRNN approach provided better results.