Confirmatory factor analysis was performed with IBM SPSS Amos 26 [
52]. A six-factor measurement model comprising health consciousness, communication effectiveness, distrust, perceived ease of use, perceived usefulness, and intention to use mHealth produced a chi-square value (
χ2) of 274.3 with 104 degrees of freedom (
df). The normed chi-square statistic (
χ2/
df) was 2.637, indicating a good fit between the model and data [
53]. Additionally, the model produced three other indices: non-normed fit index (NNFI) = 0.95, comparative fit index (CFI) = 0.97, and root mean square error of approximation (RMSEA) = 0.06. All of them met the good fit criteria for NNFI ≥ 0.95, CFI ≥ 0.95, and RMSEA ≤ 0.05 as suggested by Hair et al. [
53], and Hu and Benter [
54]. The factor loadings of items ranged from 0.65 to 0.96, except that the first item of perceived ease of use, i.e., PEOU1 only had a factor loading of 0.49. After dropping this item, the measurement model with six constructs and 16 items produced the following fit indices:
χ2/
df = 2.036 (
χ2 = 181.2;
df = 89), NNFI = 0.96, CFI = 0.98, RMSEA = 0.05, all meeting the criteria as stated above. Factor loadings ranged from 0.65 to 0.96, as shown in
Table 1.
Table 1 also presents the composite reliabilities and average variance extracted (AVE) values for the six constructs. The values of composite reliability ranged from 0.80 to 0.90, all above the threshold of 0.7, as suggested by Nunnally and Bernstein [
49]. The values of AVE ranged from 0.57 to 0.74, all above the threshold of 0.5 as suggested by Nunnally and Bernstein [
49]. All these supported the reliability of the six constructs. The values of the square root of AVE are shown in the diagonal elements of
Table 4. It was found that the value of the square root of AVE of a construct was greater than the correlations between that construct and all other constructs, supporting discriminant validity [
55].
To check the extent of common method variance and the possibility of an alternative model, we tested a second measurement model with one factor and 16 items. This one-factor measurement model produced the following fit indices: χ2/df = 18.6 (χ2 = 1934.4; df = 104), NNFI = 0.61, CFI = 0.63, RMSEA = 0.19, indicating that this one-factor model did not fit the collected data well. Thus, the six-factor model was considered appropriate.
Structural equation modeling was performed with IBM SPSS Amos 26 using maximum likelihood estimation. The structural model produced the following fit indices:
χ2/
df = 4.928 (
χ2 = 473.1;
df = 196), NNFI = 0.91, and CFI = 0.92. These indices indicated that the structural model was considered acceptable [
53]. The value of RMSEA was 0.09, which could be considered as marginally acceptable [
53].
Figure 2 shows the final structural model.
Table 5 shows the direct, indirect, and total effects of health consciousness, communication effectiveness, perceived ease of use, perceived usefulness, and distrust on people’s intention to use mHealth. This table also shows the direct effects of health consciousness, communication effectiveness, and perceived ease of use on perceived usefulness. Overall, it was found that perceived usefulness strongly and significantly influenced people’s intention to use mHealth (β = 0.74;
p < 0.001), supporting Hypothesis 2. Additionally, perceived usefulness was strongly and significantly affected by perceived ease of use (β = 0.56;
p < 0.001), health consciousness (β = 0.33;
p < 0.001), and communication effectiveness (β = 0.43;
p < 0.001), supporting Hypothesis 3, Hypothesis 4, and Hypothesis 6, respectively. Both health consciousness (β = 0.14;
p < 0.05) and communication effectiveness (β = 0.16;
p < 0.05) significantly affected people’s intention to use mHealth moderately, supporting Hypothesis 5 and Hypothesis 7, respectively. On the other hand, the direct influence of perceived ease of use on people’s intention to use mHealth was significant but negative (β = −0.13;
p < 0.01), rejecting Hypothesis 1. However, the total effect of perceived ease of use on people’s intention to use mHealth was positive because perceived ease of use had a significant, indirect, and greater effect on people’s intention to use mHealth through perceived usefulness, as shown in
Table 5. Distrust did not show significant effect on people’s intention to use mHealth (β = −0.07;
n.s.), rejecting Hypothesis 8. In terms of the total effects, the most significant predictor of people’s intention to use mHealth was communication effectiveness, followed by health consciousness and perceived ease of use. The results of hypothesis testing are summarized in
Table 6.