Unraveling the influence of product advertising on consumer buying interest: exploring product knowledge, product quality, and mediation effects

Figures & data

Figure 1. Conceptual Model. Source: Author’s Synthesis (2023).

Table 1. Variable construct.

Variable	Indicator	Code
Product Advertising	Advertising	Z1
	Private sales	Z2
	Sales promotion	Z3
	Public Relations	Z4
	From mouth to mouth	Z5
	Direct marketing	Z6
Product knowledge	Product knowledge	A1
	Buy knowledge	A2
	Usage knowledge	A3
Product quality	Show	B1
	Durability	B2
	Conformity to specifications	B3
	Feature	B4
	Reliability	B5
	Aesthetics	B6
Buying Interest	Attention	Y1
	Interest	Y2
	Hatred	Y3
	Confession	Y4

Source: Author’s Synthesis (2023).

Table 2. Outer model testing criteria.

Testing Criteria	Criteria
Convergent Validity	Loading Factor > 0.70
	AVE > 0.50
Discriminant Validity	HTM < 0.90
Reliability Estimation
Cronbach’s Alpha	>0.70
Composite Reliability (rho_c)

(Source: Hair et al., 2017; Hair et al., 2019).

Table 3. Inner model testing criteria.

Inner Model	Testing Criteria	Conclusion
Multicollinearity (Inner Model)	VIF < 10	Escaping Multicollinearity
R Square	0.25	Weak Model
	0.50	Medium Model
	0.75	Strong Model
Gooness of FIT	SRMR < 0.10	Fit Models
F Square (Effect Size)	0.02	Small/low
	0.15	Intermediate/medium
	0.35	Big/strong
Path Coefficients (Direct Effect)	p value <.05	Significant influence
	p value >.05	No significant effect

(Source: Hair et al., 2017; Hair et al., 2019).

Table 4. Convergent validity.

Items	Buying Interest	Product Advertising	Product Knowledge	Product Quality
A1			0.885
A2			0.897
A3			0.895
B1				0.830
B2				0.922
B3				0.930
B4				0.823
B5				0.935
B6				0.828
Y1	0.920
Y2	0.936
Y3	0.871
Y4	0.910
Z1		0.866
Z2		0.885
Z3		0.897
Z4		0.928
Z5		0.837
Z6		0.747

Source: (Data Analysis Using Smart PLS, 2023).

Table 5. Discriminant validity.

Variables	Buying Interest	Product Advertising	Product Knowledge
Product Advertising	0.890
Product Knowledge	0.787	0.899
Product Quality	0.937	0.849	0.764

Source: (Data Analysis Using Smart PLS, 2023).

Table 6. Reliability test.

Variables	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
Buying Interest	0.930	0.933	0.950	0.827
Product Advertising	0.930	0.932	0.945	0.743
Product Knowledge	0.873	0.882	0.922	0.797
Product Quality	0.941	0.944	0.953	0.773

Figure 2. Final model estimation results. Source: (Data Analysis Using Smart PLS, 2023).

Table 7. Collinearity statistics (VIF).

Items	VIF
A1	2403
A2	2174
A3	2482
B1	2816
B2	4751
B3	6572
B4	2830
B5	6645
B6	2827
Y1	4663
Y2	5287
Y3	2773
Y4	3427
Z1	3489
Z2	3951
Z3	4260
Z4	5178
Z5	2678
Z6	1890

Source: (Data Analysis Using Smart PLS, 2023).

Table 8. Evaluation of model component suitability.

Model Components	Critical value	Saturated Model	Information
SRMR	<0.08	0.063	Fit
d_ULS	0.820	0.747	Fit
d_G	0.485	0.756	Fit
Chi-Square	275,725	409.622	Fit

Source: (Data Analysis Using Smart PLS, 2023).

Table 9. R Square.

Variables	R Square	R Square Adjusted
Buying Interest	0.818	0.812
Product Knowledge	0.667	0.663

Source: (Data Analysis Using Smart PLS, 2023).

Table 10. F Square.

Variables	Buying Interest	Product Knowledge
Product Advertising	0.145	1999
Product Knowledge	0.002
Product Quality	0.690

Source: (Data Analysis Using Smart PLS, 2023).

Table 11. Direct effect hypothesis test results.

Hypothesis	Path	T-Statistics	p-value	Description
H1	Product Advertising -> Buying Interest	3564	.000	Significant
H2	Product Advertising -> Product Knowledge	17,543	.000	Significant
H3	Product Knowledge -> Buying Interest	0.455	.649	Not significant
H4	Product Quality -> Buying Interest	6085	.000	Significant
H5	Product Advertising -> Buying Interest	3564	.000	Significant

Source: (Data Analysis Using Smart PLS, 2023).

Table 12. Mediation effects.

Hypothesis	Path	T-Statistics	p-value	Description
H6	Product Advertising -> Product Knowledge -> Buying Interest	0.454	.650	Not mediating

Source: (Data Analysis Using Smart PLS, 2023).

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2017). Multivariate data analysis (7th ed.). Pearson.

 Google Scholar

Hair, J. F. J., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Annabel Ainscow.

 Google Scholar

Data availability statement

The data underlying the findings of this investigation can be obtained by contacting the corresponding author upon reasonable request.