Discriminant Analysis for Predicting Digital Skills in the Work of Fourth-Year Digital Marketing Students, Southeast Bangkok University
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Abstract
The objectives of this research were to examine the discriminant analysis for prediction of digital skills in work of fourth-year digital marketing students in the group with digital skills in work and the group that needs to develop digital skills in work. The sample group consisted of 73 fourth-year digital marketing students from the Faculty of Accountancy and Management Science at Southeast Bangkok University who were selected through a simple random sampling method using a lottery method. The data collection instrument was a questionnaire. The data was analyzed using descriptive statistics including frequency, percentage, mean, and standard deviation, as well as multivariate statistics through discriminant analysis with the Enter method, employing Box’s M test, Eigenvalue, Canonical Correlation, and Wilks’ Lambda.
The findings were found that factors affecting the digital skills in work of the 4th year digital marketing students consisted of 6 factors: perceived usefulness of digital technology (PU), self-efficacy of digital technology (SE), social environment (EN), attitude and value (AV), knowledge of digital marketing (KDM), and perceived ease of use of digital technology (PEoU). The classification equations were able to predict the membership of the group of students with digital skills in work with 86.27 percent accuracy and the group of students who need to develop additional digital skills in work with 86.36 percent accuracy, and predicted the membership of both groups with 86.30 percent accuracy. The classification equations were as follows:
The equation of the raw scores was:
Y = -12.096 + 1.235(PU) + 0.218(PEoU) - 0.268(KDM) + 0.464(AV) + 0.732(SE) + 0.552(EN)
The equation of the standard scores was:
Z = 0.477(PU) + 0.101(PEoU) - 0.145(KDM) + 0.215(AV) + 0.358(SE) + 0.253(EN)
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References
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