Opt-in e-mail marketing influence on consumer behaviour: A Stimuli–Organism–Response (S–O–R) theory perspective

Figures & data

Figure 1. Conceptual model.

Source: Researchers’ conception (2021)

Table 1. Sample Characteristics

Male	155	38.8%
Female	243	60.9%
Prefer not to say	1	0.3%
Total	399
Age
18–25	101	25.3%
26–35	163	40.9%
36–45	99	24.8%
46–55	30	7.5%
55 and above	6	1.5%
Total	399
Very well-off	59	14.8%
Somewhat well-off	216	54.1%
Not so well-off	103	25.8%
Not well-off at all	21	5.3%
Total	399
Matriculation	3	0.8%
Diploma/Certificate	8	2.0%
Undergraduate degree	148	37.1%
Postgraduate degree (Honours, Masters, Doctorate)	240	60.2%
Total	399
What do you do when you receive “permission-based/opt-in” promotional or informational e-mail marketing messages?
Disregard it completely	55		13.8%
Read it occasionally	212		53.1%
Delete it after accumulating too many of them	24		6.0%
Delete it when I get time	85		21.3%
Delete it right away	23		5.8%
	399
Which sender do you mostly receive “permission-based/opt-in” promotional or informational e-mail messages from?
Vodacom	55		13.8%
Ster-Kinekor	6		1.5%
Pick n Pay	42		10.5%
Woolworths	71		17.8%
Edgars	9		2.3%
Discovery Insure	20		5.0%
Makro	22		5.5%
Estee Lauder	3		0.8%
Samsung	23		5.8%
BMW	3		0.8%
ADT Security	5		1.3%
Pam Golding Properties	3		0.8%
Additional	137		34.3%
	399

Source: Primary data (2021)

Table 2. Accuracy analysis scale

Research Construct		Descriptive Statistics				Cronbach’s Test		C.R. Value	AVE Value	Factor Loading
		Mean Value		Standard Deviation		Item -total	α value
INV	INV1	4.510	4.358	1.655	1.716	0.775	0.848	0.833	0.745	0.888
	INV2	4.510		1.670		0.779				0.892
	INV3	4.710		1.610		0.756				0.825
	INV4	3.700		1.930		0.482				0.526
ENT	ENT1	3.710	3.727	1.803	1.798	0.751	0.838	0.812	0.818	0.828
	ENT2	4.360		1.633		0.681				0.806
	ENT3	3.110		1.957		0.681				0.766
LYT	LYT1	5.070	5.058	1.528	1.486	0.856	0.953	0.898	0.912	0.881
	LYT2	5.090		1.517		0.902				0.923
	LYT3	5.060		1.463		0.894				0.927
	LYT4	5.010		1.435		0.893				0.916
VAL	VAL1	5.010	5.090	1.492	1.437	0.827	0.924	0.923	0.820	0.871
	VAL2	5.260		1.391		0.761				0.795
	VAL3	4.910		1.482		0.822				0.868
	VAL4	5.000		1.446		0.869				0.913
	VAL5	5.270		1.372		0.732				0.771
ATT	ATT1	4.020	4.540	1.976	1.815	0.667	0.786	0.793	0.740	0.847
	ATT2	5.240		1.611		0.594				0.647
	ATT3	4.360		1.859		0.629				0.727
INT	INT1	4.180	4.320	1.822	1.714	0.744	0.881	0.823	0.780	0.785
	INT2	3.740		1.878		0.719				0.758
	INT3	4.710		1.508		0.791				0.871
	INT4	4.650		1.648		0.734				0.838

Source: Primary data (2021)

Table 3. Correlation Matrix

Correlations Matrix
	INV	ENT	LYT	VAL	ATT	INT
INV	1
ENT	.730**	1
LYT	.517**	.518**	1
VAL	.491**	.555**	.742**	1
ATT	.542**	.637**	.477**	.477**	1
INT	.615**	.719**	.569**	.593**	.705**	1
** significance is at 0.01

Source: Primary data (2021)

Table 4. Model fit indexes

Model	Goodness of fit	Normative Fit	Relative Fit	Incremental Fit	Tucker Lewis	Confirmatory Fit	RMSEA
Default model	0.920	0.941	0.921	0.964	0.951	0.963	0.061

Source: Primary data (2021)

Table 5. Hypotheses results and path coefficients

			Hypothesis	Estimate	P Value
INV	→	ATT	H1	−0.021	0.518	Not Supported
ENT	→	ATT	H2	0.897	***	Supported
LYT	→	ATT	H3	0.189	***	Supported
VAL	→	ATT	H4	0.083	0.009	Supported
ATT	→	INT	H5	0.977	***	Supported

Source: Primary data (2021)