Application of Social Cognitive Career Theory to Investigate the Effective Factors of the Career Decision-Making Intention in Iranian Agriculture Students by Using ANN

Abstract

The main purpose of this study was to determine the factors that affect the career decision-making intention of agriculture students of Kermanshah University based on Social Cognitive Career Theory (SCCT), by using Artificial Neural Network (ANN). The research population included agriculture students ( N = 1,122). Using stratified random sampling, a sample of 288 was constituted. Data were collected using a questionnaire, which consisted of four parts: Career Decision-Making Self-Efficacy (CDMSE), Career Decision-Making Outcome Expectation (CDMOE ), Career Exploratory Plans or Intentions (CEPI), and NEO Five Factor Inventory (NEO-FFI). Back translation was used for validity, and reliability was assessed using Cronbach’s alpha coefficient. To analyze the data, statistical methods and ANN with MATLAB software were used. On the basis of trial and error, a network, including three layers with one hidden layer with 20 neurons, Levenberg–Marquardt training algorithm, and sigmoidal transfer functions, was selected to construct the network of career decision-making intention. After training and simulation, the validation of the network was tested by linear regression ( R = .999). For assurance of the generalization, the network was tested again. Finally, analysis of variance was used to compare the network output.