Search results

This paper extends entrepreneurial ecosystems theory by testing how aspects of the local business environment affect individual entrepreneurs' ability to translate their personal resources into firm performance.

Data were collected from 223 business owners across Australia. Moderation hypotheses were tested using multiple hierarchical regression and confirmed with the Preacher and Hayes (2004) bootstrapping method.

The results show that business owners' psychological capital, social capital and entrepreneurial education directly affect their individual firm performance. These positive relations are moderated by specific aspects of the business environment, such that they are stronger when the environment is more favorable.

This study puts individual business owners back into entrepreneurial ecosystems theory and explains how they can make the most of their personal resources, suggesting a complex interplay where one size does not fit all. Far-reaching practical implications for policymakers are discussed.

This study explored the experiences of Irish emergency medical services (EMS) first responders during the first nationwide restrictions to curb the spread of COVID-19.

A systematic literature review (SLR) of research into healthcare workers' and first responders' experiences during the COVID-19 and 2003 SARS pandemics was performed. The SLR informed the content of an online questionnaire distributed via the Irish Pre-Hospital Emergency Care Council to 2,092 first responders on its live register. Data analysis used both descriptive and content analysis.

EMS first responders faced many challenges including PPE quality, training on its use, issues with decontamination facilities, and organisational effectiveness. Emotional challenges included the anxiety experienced, the impact on families, and ethical dilemmas confronted related to patient care. Positive findings also emerged, such as first responders' dedication to working through the pandemic, collegiality, and the community goodwill displayed.

While investigations of the impact of the COVID-19 pandemic on healthcare workers have been undertaken globally, studies focussing exclusively on the experiences of EMS first responders have been rare. This study addressed this knowledge gap, providing an insight into the challenges and successes experienced by first responders and identifying opportunities for learning that can be applied to future public health emergencies.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.