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Structural equation modeling using partial least squares algorithm in educational and psychological research: an applied example to test a structural model of the relationships between artificial intelligence use, therapeutic alliance, and job engagement among mental health service providers
DOI:
https://doi.org/10.51930/jcois.21.2024.79.0499Keywords:
Partial least squares structural equation modeling, artificial intelligence, therapeutic alliance, functional integration, mental health providersAbstract
Structural equation modeling using the Partial Least Square-Structural Equation Modeling algorithm (PLS-SEM) is a statistical technique that has gained attention recently due to its flexibility and predictive power. However, there is a scarcity of guidance on applying this technique in the field of social sciences, especially education and psychology research. Therefore, this study aimed to apply the PLS-SEM algorithm as an advanced approach to structural equation modeling, highlight the justifications for its use and compare it to structural modeling using the variance method. PLS-SEM was applied using the SmartPLS program to a proposed structural model of the causal relationships between artificial intelligence use, therapeutic alliance, and job engagement. The descriptive approach was applied. The study sample consisted of (127) mental health service providers in the Kingdom of Saudi Arabia, including 58 males and 69 females aged 25-50 years. (36.32±6.43), the artificial intelligence questionnaire, therapeutic alliance scale, and job engagement scale were applied to them, all prepared by the researcher. The results found there were median levels of artificial intelligence use, therapeutic alliance, and job engagement, and also showed that the proposed structural model of artificial intelligence use and the therapeutic alliance has a good ability to predict job engagement and explain the interrelationships between them compared to the indicators model and the linear model. The results also revealed a strong overall positive effect statistically significant (p< 0.05) for the variable of attitude towards using artificial intelligence in the therapeutic alliance (0.941) and job engagement (0.930), and a moderate overall positive effect statistically significant (p< 0.05) for the therapeutic alliance in job engagement (0.694). These findings indicate the importance of integrating training of mental health providers on the skills of using artificial intelligence and generative artificial intelligence techniques into professional practice to develop their ability to build a therapeutic alliance with beneficiaries and enhance their sense of the importance of the profession and their well-being in it.
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