Wella Jayanti’s research while affiliated with Islamic University of Riau and other places

Orientation: Substantial investment in entrepreneurship education did not resolve the issue of only 50% of vocational graduates finding employment, highlighting the need to foster entrepreneurial pursuits.Research purpose: To examine the impact of achievement goals on the entrepreneurial psychological readiness (EPR) of vocational high school students, as mediated by psychological capital in Pekanbaru, Indonesia.Motivation for the study: This study involved 378 vocational high school students from all available vocational fields. Data were collected with the assistance of class teachers after completing administrative processes with the school.Research approach/design and method: This research employs a quantitative methodology and is assisted by SEM-SmartPLS statistical analysis. The data collection instruments consist of four components: self-report, the Goal Measure-Revised (AGQ-R), 12 items; the PsyCap questionnaire (PCQ), 12 items and the EPR, 24 items. A two-step partial least squares structural equation modelling (PLS-SEM) approach was used to evaluate the measurement and structural models.Main findings: The relationship between achievement goals and entrepreneurship psychological readiness is completely mediated by psychological capital that this hypothesis indicates. We conclude that the relationship between achievement goals and psychological readiness for entrepreneurship through psychological capital shows a significant influence.Practical/managerial implications: The study underscored the critical role of psychological capital in enhancing entrepreneurial readiness and suggested that balancing goal orientation with psychological well-being was essential for developing entrepreneurial potential among vocational students.Contribution/value-add: This study underscores the importance of psychological capital in fostering entrepreneurial readiness, revealing its indispensable role in achieving entrepreneurial success.

Machine learning has become an exciting topic in psychology-related research, one of which is counseling psychological readiness for entrepreneurship. An intelligent application developed using a machine learning model to assist the counseling process in measuring a person's psychological readiness for entrepreneurship. This application was generated using the Entrepreneurship Psychological Readiness (EPR) instrument. In this study, to get the most suitable machine learning model, a comparison of 2 (two) machine learning models, namely, Naïve Bayesian (NB) and k-Nearest Neighbor (k-NN), involving 1095 training data. There are 4 (four) prediction classes recommended from the results of counseling: categories not ready for entrepreneurship, given training, guided, and prepared for entrepreneurship. The EPR instrument consists of 33 question items to measure 8 (eight) parameters used as inputs for the prediction process. The data has been randomized, and the experiment has been repeated 5 (five) times to check the consistency of performance of all techniques. 80% of the data was used as training data, and the other 20% was used as testing data. The results of the five (5) trials show that the Naïve Bayesian model provides the most consistent results in predicting a person's psychological readiness for entrepreneurship, with 89.58% accuracy, in testing. Therefore, the Naïve Bayesian model is recommended to be used in psychological counseling to predict a person's readiness for entrepreneurship

The purpose of this study was to develop an instrument for assessing psychological readiness for entrepreneurship. A well-designed measurement of entrepreneurship psychological readiness can provide early warning to policymakers, in this case the government, and provide education and funding to prospective entrepreneurs who must not only be examined physically, but also psychologically. Using Exploratory Factor Analysis (EFA) and reliability analysis, the validity and reliability of the Entrepreneurship Psychological Readiness (EPR) instrument were examined. An Exploratory Factor Analysis (EFA) found that the Entrepreneurship Psychological Readiness (EPR) instrument’s eight-factor model explained 57.44% of the variance among the items. To develop a fit model, it was necessary to exclude 26 items from the questionnaire, leaving 59 items left. The factors name identified by Personal Knowledge, Personal Adversity, Committed Certain Action, Willingness to Learn, Personal Relationship to Others, Personal Growth, Passion Achieved, and Related Person Support. All of the eight-factor models have excellent reliability of 0.96.

This study aims to create a decision tree model using machine learning to predict psychological readiness for entrepreneurship in college graduates. This research was conducted through several stages of research. In the early stages, a survey was conducted on 700 students from several universities in Riau aged between 17-25 years. The survey was conducted using the Entrepreneur Psychology Readiness (EPR) instrument. Furthermore, the survey data was validated and obtained 604 valid data to be used in forming machine learning models The urgency of this research is to find a number of decision rules from the best decision tree model to be used in building AI-based counseling applications in measuring entrepreneurial psychology readiness for college graduates. In this research, the decision tree model that is formed is divided into 2 models, namely: decision tree with pruning model and decision tree with unpruning. The pruning decision tree model produces 180 decision rules, while the unpruning model produces 121 decision rules. Good accuracy results are obtained in the pruned decision tree, which is above 99% in the use training set mode, and 82.87% in the percentage split mode. Meanwhile, the accuracy results on the unpruned decision tree are 90.18% with the use training set mode test, and 80.38% in the percentage split mode. The decision tree model with pruning technique has better performance than the unpruning decision tree model.