A Causal Model of Factors Affecting Government Nurses’ Intentions to Use Artificial Intelligence for Counseling in Thailand

Abstract

This study aimed to develop and validate a causal model and to examine the direct and indirect effects of factors influencing nurses’ intention to use artificial intelligence (AI) for counseling. The research instrument was a questionnaire whose content validity was evaluated by three experts. The Item–Objective Congruence (IOC) indices for all items ranged from 0.67 to 1.00. The reliability of the instrument was assessed using Cronbach’s alpha coefficients ranging from .935 to .968. Data were collected through an online questionnaire administered to 396 public-sector nurses selected using a proportionate multistage sampling technique. Descriptive statistics, including mean scores, were used for data analysis. Structural Equation Modeling (SEM) was conducted using LISREL software to test the proposed causal model.

The findings indicated that the proposed causal model fit the empirical data well (χ² = 22.18, df = 19, p = .28, χ²/df = 1.17, RMSEA = .02, RMR = .01, and CFI = 1.00). Regarding total effects, environmental factors exhibited the strongest influence on nurses’ intention to use AI for counseling (TE = .94), followed by personal factors (TE = .13), both of which were statistically significant at the .01 level. In terms of direct effects, environmental factors exerted the greatest direct influence (DE = .85), followed by personal factors (DE = .67), with both effects reaching statistical significance at the .01 level. Regarding indirect effects, environmental factors had the strongest indirect influence through personal factors, with an effect size of .09, which was statistically significant at the .01 level. Together, environmental factors (ENV) and personal factors (PEF) explained 90% of the variance in public-sector nurses’ intention to use AI for counseling (OBI).