A Study on Factors Influencing MBA Decision After Bachelor’s Using Hybrid Model Combining ANN With Principal Component Analysis

Abstract

This study comprehensively examines the complex relationship between various individual characteristics and individuals’ expectations after obtaining a bachelor’s degree, particularly in determining whether to pursue a Master of Business Administration (MBA). Utilizing a well structured, hypothesized framework, we examine several key variables directly influencing this critical decision-making process. The examined variables include, but are not limited to, demographic information, academic background, prior work experience, financial considerations, and overarching career aspirations. By examining these factors, we aim to uncover prevailing trends and identify the primary drivers influencing MBA enrollment decisions. To enhance the accuracy of predictions regarding MBA decisions, we have developed an innovative Artificial Neural Network (ANN) approach tailored specifically for the primary modeling phase of this research. This model integrates seven distinct learning methodologies that work in concert to enhance the overall efficacy of the ANN framework. The findings from our comparative analysis demonstrate that the hybrid model combining ANN with Principal Component Analysis (PCA), referred to as the ANN-PCA model, achieved remarkably high prediction accuracy, with key performance indicators reported as follows: an accuracy rate of 98%, precision of 94%, a recall of 96%, an F1 score of 95%, a Mean Square Error (MSE) of 0.03, and a Root Mean Square Error (RMSE) of 0.01. These impressive results significantly surpass those attained by the ANN model used in isolation, underscoring the substantial potential of the newly developed hybrid model. The insights from this study enrich the existing knowledge regarding educational decision-making processes and provide valuable guidance for implementing strategies to attract a diverse range of candidates to MBA programs.