The impact of social media on consumer-brand loyalty: A mediating role of online based-brand community

Figures & data

Figure 1. A network of online based-brand community impact on consumer-brand loyalty.

Table 1. Demographic profile of respondents

Demography	Frequency	Percentage (%)
Gender Male Female	66 56	53.9 46.1
Age 18–22 23–26 27+	42 29 51	33.7 24.1 41.6
Education Level Bachelor Master’s PhD Others	63 39 8 12	51.7 31.5 6.7 10.1
Social media platform engaged Instagram Facebook Twitter Linked-in Others	78 113 28 42 34	64.0 93.3 22.5 33.7 38.2
Average hours spent Less than 1 hour per day Between 1 to 3 hours per day Between 3 to 5 hours per day 5 hours and more	19 40 41 22	15.7 32.6 33.7 18.0

n = 122, Sources: Authors’ survey.

Table 2. Definition and summary of research constructs and their measured items

Construct	Definition	Measured items	Literature sourced
Social media platform(SMP)	A marketing communication channel uses to promote a product or service over the internet. Example, Facebook, Twitter, LinkedIn, Instagram, Pinterest, YouTube, etc.	SMP1: Social media an easy platform where I can engage in. SMP2: Social media influence you to join an online group connected to a brand or product. SMP3: Social media based-brand community has led to the creation of a virtual community (interaction over the internet)	Goh et al. (2013), Hutter et al. (2013), Laroche et al., 2012), McAlexander et al., 2002); Muniz and O’guinn (2001), Ul Islam, Rahman, & Hollebeek, (2017), and Gong (2018)
Online based-brand community(OBCC)	A brand community is a specialized, non-geographically bound community, based on a structured set of social relations among admirers of a brand” and it represents a form of association embedded in the consumption context positioned around product/brand or even a company	OBBC1: Joining online based-brand community makes you feel a sense of belongingness OBBC2: Online based-brand community leads to brand posts by the brand owner OBCC3: Brand community accessibility and familiarity in social media can influence your purchasing attitude towards brand	(Muniz & O’Guinn, 2001,) Goh et al. (2013), Hutter et al. (2013), Laroche et al., 2012), McAlexander et al. (2002), Ul Islam, Rahman, & Hollebeek, (2017), and Gong (2018)
Consumer-brand engagement(CBE)	It’s a form of relationship marketing where the bond between consumers and brands strengthens as the level of engagement heightens.	CBE1: Consumer brand engagement has the power to transform consumer behavior CBE2: Consumer-brand engagement leads to brand consistent engagement brand owner and consumer	Goh et al. (2013), Hutter et al. (2013), Laroche et al., 2012), McAlexander, Schouten, & Koenig, 2002); Muniz and O’guinn (2001), Ul Islam, Rahman, & Hollebeek, (2017), and Gong (2018)
User-brand relationship(UBR)	The forming of an intimate relationship between brand and customer (or user of a brand) is often completed in a series of stages of increasing intimacy. It also refers to the relationship that consumers, think, feel, and have a product or company brand.	UBR1: User brand relationship on social media platform makes brand(product) identification easier. UBR2: User-brand relationship in social media platforms makes product descriptions more understanding. UBR3: User-brand relationship on a social media platform makes seller-buyer interaction more friendly.	Tuškej, Golob, and Podnar (2013), Hsieh and Wei (2017), and Gong (2018)
Consumer brand promise and trust(BPT)	A brand promise is a statement made by an organization to its customers stating what customers can expect from their product and services. For example, by being the brand that communicates honestly, transparently, and consistently delivers on its promises, it could become your industry’s trusted choice.	CBPT1: Constant engagement on social media based-brand community leads to brand promise and trust CBPT2: Social media based-brand community offers the needed services to its members’ satisfaction.	Bigne, Andreu, Hernandez, and Ruiz (2018), Esch, Langner, Schmitt, and Geus (2006), Adjei et al. (2010), Mahrous and Abdelmaaboud (2017), Hennig-Thurau et al. (2010), Adjei et al. (2010)
Consumer-brand loyalty(CBL)	Brand loyalty is a pattern of consumer behavior through which consumers tend to get committed to a specific brand or product and make repeat purchases over time.	CBL1: User-brand relationship in the online social media-based brand community leads to brand affection (love) CBL2: Feature content in social media based-brand community influence your purchasing attitude towards a brand	Bigne et al. (2018), Esch et al. (2006), Adjei et al. (2010), Mahrous and Abdelmaaboud (2017), Hennig-Thurau et al. (2010), Adjei et al. (2010)

Table 3. Result of the PCA of the study constructs

		Components
		1	2	3	4
CBPT1		0.836
CBL2		0.682
CBE2		0.675
CBL1		0.654
UBR3		0.52
SMP3			0.886
SMP1			0.85
OBBC1			0.612
UBR2				0.705
OBBC3				0.688
CBPT2				0.672
UBR1				0.504
OBBC2
SMP2					0.868
CBE1					0.583
Initial eigenvalues		4.853	1.504	1.230	1.112
% of Variance Explained		32.354	10.029	8.198	7.414
Cumulative % of Variance Explained -> 57.994
Extraction Method: Principal Component Analysis.
^a Rotation converged in 5 iterations.
Kaiser-Meyer-Olkin (KMO) Measure Sampling of Adequacy -> (0.811)
Bartlett’s Test of Sphericity:
Approximate chi-Square value -> 565
df -> 105
Sig. -> 0.000

Source: Authors’ Elaboration extracted from SPSS

Table 4. Quality criteria for outer reflective model

Latent variable	Indicators	Loadings	Bootstrapped T-value (loadings)	Composite Reliability	AVE	Cronbach Alpha
	CBE1	0.820381	13.017	0.827322	0.705608	0.583843
CBE	CBE2	0.859181	19.134
	CBL1	0.780906	11.869	0.793907	0.658556	0.483472
CBL	CBL2	0.84101	17.352
	CBPT1	0.895374	30.053	0.780276	0.643284	0.466096
CBPT	CBPT2	0.696329	7.207
	OBBC1	0.790293	16.204	0.761193	0.517301	0.533592
OBBC	OBBC2	0.729387	9.149
	OBBC3	0.628757	4.977
	SMP1	0.908032	27.056	0.808463	0.607539	0.617132
SMP	SMP2	0.410361	2.662
	SMP3	0.910878	25.888
	UBR1	0.794711	16.72	0.797714	0.569894	0.625377
UBR	UBR2	0.656993	5.224
	UBR3	0.804038	16.194

Source: Authors’ Elaboration extracted from SmartPLS

Table 5. Test of discriminant validity (Fornell-Lacker’s Criterion), R2, Mean and SD

Construct	Mean (SD)	CBE	CBL	CBPT	OBBC	SMP	UBR
CBE	3.855 (0.916)	0.840*
CBL	3.690 (0.861)	0.466501	0.748*
CBPT	3.885 (0.791)	0.482721	0.62051	0.796*
OBBC	3.810 (0.886)	0.392336	0.409952	0.369227	0.716*
SMP	3.787 (0.894)	0.3392	0.328171	0.318678	0.540908	0.909*
UBR	3.997 (0.856)	0.366573	0.506779	0.461701	0.574545	0.432491	0.752*

N = 122 users of social media associated with the brand community.

*The highlighted diagonal values indicated the square root of average variance extracted (AVE) for each construct, the other values represent the correlation among constructs.

Sources: Authors’ Elaboration extracted from SmartPLS.

Table 6. Estimate of the hypothesized structural path model significance

hypothesis	Hypothetical path	Coefficient	S.E	T-Statistics	Test outcome
H1	SMP—> OBBC	0.541	0.083	8.433	Supported
H2	SMP—> CBPT	0.069	0.098	0.769	Not supported
H3a	OBBC—> CBE	0.392	0.096	3.963	Supported
H3b	OBBC—> UBR	0.575	0.077	8.574	Supported
H4a	CBE—> CBPT	0.348	0.090	3.750	Supported
H4b	UBR—> CBPT	0.304	0.103	2.894	Supported
H5	CBPT—> CBL	0.621	0.074	8.408	Supported

Source: Authors’ Elaboration extracted from SmartPLS

Figure A1. Visual description of estimated structural model Source: Authors’ Elaboration extracted from SmartPLS.

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