Determining the drivers of continued mobile food delivery app (MFDA) usage during a pandemic period

Figures & data

Figure 1. Conceptual framework for the predictors of continuance intention.

Table 1. Convergent validity.

Latent	Indicator	Standardised loadings	Alpha	CR	AVE
Continuance	CI_1	0,861	0.908	0.910	0.718
	CI_2	0,846
	CI_3	0,782
	CI_4	0,894
Habit	Hab_1	0,822	0.850	0.803	0.578
	Hab_2	0,743
	Hab_3	0,758
Hedonic	Hed_1	0,866	0.890	0.891	0.732
	Hed_2	0,843
	Hed_3	0,859
Performance	Perf_1	0,797	0.898	0.892	0.674
	Perf_2	0,834
	Perf_3	0,854
	Perf_4	0,85
TTF	TTF_1	0,848	0.887	0.892	0.674
	TTF_2	0,864
	TTF_3	0,858
	TTF_4	0,704

Table 2. HTMT analysis.

	Habit	Hedonic	Performance	TTF
Habit
Hedonic	0.809
Performance	0.719	0.711
TTF	0.682	0.730	0.872

Note: Thresholds are 0.850 for strict and 0.900 for liberal discriminant validity.

Table 3. Results of hypotheses testing.

Alternative hypotheses	SRW	P value	Result
H1: Habit → Continuance intention	0.480**	0.000	Supported
H2: Hedonic motivation → Continuance intention	−0.074	0.051	Not supported
H3: Performance expectancy → Continuance intention	0.849**	0.000	Supported
H4: TTF → Continuance intention	−0.210	0.793	Not supported
H5: TTF → Performance expectancy	0.884**	0.000	Supported

Notes: SRW- Standard regression weight; **Significant at p < 0.05.

Table 4. Results of the mediation effect.

Bootstrapping
			Bias-Corrected 95% CI			Percentile 95% CI
Point estimate			Lower	Upper	p-value	Lower	Upper	p-value
TTF≫PE≫CI	Indirect effect	0.751	0.564	0.023	0.077	0.569	1.027	0.010	CM
TTF≫CI	Direct effect	−0.210	−0.473	1.023	0.012	−0.480	0.007	0.058

Table 5. Moderation results.

Low experience
	Chi-square (constrained model)	Chi-square (unconstrained model)	DIFF	Moderation
Habit	427.6	391.6	36	YES
Hedonic	421.2	391.6	29.6	YES
Performance	391.9	391.6	0.3	NO
TTF	437.6	391.6	46	YES
High experience
	Chi-square (constrained model)	Chi-square (unconstrained model)	DIFF
Habit	438.7	410.8	27.9	YES
Hedonic	486.1	410.8	75.3	YES
Performance	412.5	410.8	1.7	NO
TTF	455.8	410.8	44.7	YES