This article aims to determine factors crucial for the performance of new ventures by performing a meta-analysis. It evaluates the performance of machine learning and statistical models by selecting 19 studies through preferred reporting items for systematic reviews and meta-analyses (PRISMA). The results obtained were graphically represented using forest plots. A subjective analysis of the studies revealed “business plan,” “market scope,” “team size,” “service timing,” “market growth,” and “age of the entrepreneur” as crucial factors for deciding the performance of the new ventures. In machine learning model-based studies, the combined model was found to be statistically significant. The variables, namely, “seed funding,” “funding rounds,” “location,” “social media presence,” “team size,” “number of founding members,” and “defunct date” affect the predictive capability of these models. The implications of this article can be applied by start-ups to reduce uncertainty and enhance performance.

In this study, we analyze the number of infected positive cases of COVID-19 outbreak with concern to lockdown in India in the time window of February 11th 2020 to Jun 30th 2020. The first case in India was reported in Kerala on January 30th 2020. To break the chain of spreading, Government announced a nationwide lockdown on March 24th 2020, which is increased two times. The Ongoing lockdown 3.0 is over on May 18th, 2020. We derived how the lockdown relaxation is going to impact on containment of the outbreak. Here the Exponential Growth Model has been used to derive the epidemic curve based on the data collected from February 11th 2020, to May 11th 2020, and the Machine Learning based Linear Regression model that gives the epidemic curve to predict the cases with the continuous flow of the lockdown. We estimate that if the lockdown is continuing with more relaxation, then the estimated infected cases reach up to 1.16 crores by June 30th 2020, and the lockdown would persist with current restriction, then the expected predicted infected cases are 5.69 lacs. The Exponential Growth Model and the Linear Regression Model are advantageous to predict the number of affected cases of COVID-19. These models can be used for forecasting in long term intervals. It shows from our result that lockdown with certain restriction has a vital role in preventing the spreading of this epidemic in this current situation.

Various significant features influence the success of a startup in an entrepreneurial ecosystem. However, identification of such features has not been performed coherently. This study aims to bridge the gap by determining the essential features impacting a startup's success. In the first part, a machine learning-based approach is applied empirically to determine critical features from ten datasets. Then the obtained features were validated using popular feature selection techniques based upon Recursive Feature Elimination (RFE), SelectKBest, and XGBoost to assure the final significant features. Furthermore, mutual information is used between the features obtained from the proposed approach and the ground truth to measure the approach's efficacy, which is calculated to be 90%. The significant features acquired through this approach imply how the entrepreneurs prioritise the determining features in startup success and maximise the socio-economic outputs. This study provides an essential contribution to the entrepreneurial finance literature by empirically exploring the features influencing a startup's success based on its financial performance in different stages of its lifecycle. Startups can apply the implications of this study to reduce uncertainty and enhance performance. It also helps founders, investors, and researchers understand a startup's performance and plan future steps accordingly.