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The main purpose of this study was to determine the factors thataffect the career decision-making intention of agriculture students of KermanshahUniversity based on Social Cognitive Career Theory (SCCT), by using Artificial NeuralNetwork (ANN). The research population included agriculture students (N = 1,122). Usingstratified random sampling, a sample of 288 was constituted. Data were collected using aquestionnaire, which consisted of four parts: Career Decision-Making Self-Efficacy(CDMSE), Career Decision-Making Outcome Expectation (CDMOE ), Career Exploratory Plansor Intentions (CEPI), and NEO Five Factor Inventory (NEO-FFI). Back translation was usedfor validity, and reliability was assessed using Cronbach’s alpha coefficient. Toanalyze the data, statistical methods and ANN with MATLAB software were used. On thebasis of trial and error, a network, including three layers with one hidden layer with20 neurons, Levenberg–Marquardt training algorithm, and sigmoidal transfer functions,was selected to construct the network of career decision-making intention. Aftertraining 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

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