Effects of Social Media Marketing and Selected Marketing Constructs on Stages of Brand Loyalty

Abstract

The main purpose of this study is to examine the antecedents of brand loyalty in the context of fast fashion industry. This study also highlights the role of social media (SM) marketing on value consciousness (VC), brand love (BL) and brand consciousness (BC). A self-structured questionnaire survey method was employed to collect data from 240 customers in Malaysia. Statistical analysis is performed based on the PLS-SEM approach to analyze the data. The results reveal that SM marketing has a significant effect on BC and BL in the fast fashion industry. The result demonstrates that SM has indirect positive impact on different stages of brand loyalty through BL and BC.

This study is the first that investigates the impacts of social network marketing activities, VC, BL and BC on stages of brand loyalty. The findings can help marketers develop effective marketing strategies in order to build brand loyalty especially in the fast fashion industry.

Keywords

Brand loyalty social network marketing brand love fast fashion

Introduction

For decades, in any business, the primary goal is to build a strong brand relationship with customers, where they can succeed and gain long-term competitive advantage in the marketplace (Bapat & Thanigan, 2016). Different strategies have been developed to retain the customer and reduce the percentage of customers switching from one brand to another (Zhang, Van Doorn, & Leeflang, 2014). However, in emerging markets like Malaysia, consumers keep switching between different brands, and for this reason, scholars have been focusing on brand loyalty and its association with different business domains such as purchase behaviour, purchase intention, marketing and branding (So, Parsons, & Yap, 2013). According to Matthews, Son, and Watchravesringkan (2014), in business, maintaining customers’ loyalty towards a brand costs less than having to attract new customers. Salem and Salem (2018) stated that the value of a brand is a composite of its functional issues and its symbolic issues. Scholars reveal that loyalty is a consequence of an information process of three levels: level one is cognitive loyalty, level two is the affective loyalty (AL), and the highest level is conative loyalty, which are influenced by consumers’ perception towards a brand (El-manstrly, 2016; Oliver, 1999).

In recent years, the fast fashion industry has significantly evolved. Many brands have changed their business model to adopt the fast fashion model, which forced companies to adopt the latest fashion trend to align with consumers’ demand at a reasonable price (Bhardwaj & Fairhurst, 2010). A study indicated that consumer behaviour towards fast fashion is not fully understood and explored, and they recommended implementing empirical research to understand consumer behaviour towards fast fashion. It is very important to investigate how brand loyalty is developed throughout the different stages for consumers who have different types of consciousness such as brand consciousness (BC) and value consciousness (VC) (Zhang et al., 2014). In order to gain deeper insight into brand loyalty and to fill the research gap, this research attempts to examine the relation between networking media marketing (SM) and brand loyalty stages through VC, BC and brand love (BL). The study also focuses to highlight the role of SM and its relationship with VC, BC and BL, which eventually create a stronger customer loyalty towards a fast fashion brand.

The study would be beneficial for fast fashion governance and management to establish proper branding and marketing strategies where managers can have better insights into consumers’ behaviour and its precedencies and can set effective consumer marketing and engagement strategies and tools. The first section of the manuscript discusses the extant literature. The second section represents the objective and rationale of the study. Furthermore, the next section discusses the research method. The results of the study are explained in the fourth section. Finally, the article concludes with the implications, limitations and future directions.

Review of Literature

Social Media Marketing Activities

In recent years, organizations have considered social media as one of the important platforms that could create business success (Vernuccio et al., 2015), but most of these organizations are finding it difficult to build successful brand loyalty over the social media (Ismail, 2017). Social network marketing is defined as using the social media platform to promote a product or services and increase the visibility on the Internet, which may assist in creating a social network for exchanging ideas and knowledge (Becker, Nobre, & Kanabar, 2013). Although digital marketing activities include different strategies such as e-mails, promotion strategies through websites, SM marketing is considered one of the most successful strategies to meet branding goals (Habibi, Laroche, & Richard, 2014). To add on, organizations are using the social media platform to convert users to be part of their advertisement campaign and encourage them to be more engaged and share their ideas through this platform (Becker et al., 2013).

Consumers use social media in different ways (Dijkmans, Kerkhof, & Beukeboom, 2015). For instance, some people use social media for information search, to engage with brand community or even to find particular products with low prices (Mowbray, Hall, Raeside, & Robertson, 2017). Moreover, value-conscious consumers mostly prefer to use the social media to find products with low prices (Zielke, 2014). Zielke (2014) argued that value-conscious consumers are more engaging on social media by sharing, liking or even for giving feedbacks towards brands. In addition, social network enables consumers to compare prices and product features among different brands (Martinho, Pires, Portela, & Fonseca, 2015). Thus, we can argue that better marketing strategies through the social media platform, which would enable users to find easily products of reasonable price and a lot of feedback regarding a brand, would improve VC (Jayasuriya, Azam, Khatibi, Atan, & Dharmaratne, 2018; Mukherjee & Banerjee, 2017). Hence, the study proposes the following hypothesis: social network marketing positively has an impact on the construct of value conscious.

Consumer preferences for selecting a particular brand’s product differ from one consumer to another (Madzharov, Block, & Morrin, 2015; Salem & Chaichi, 2018). There are consumers who choose a product because they recognized the brand due to its extremely promoted brand style (Heckler, Keller, Houston, & Avery, 2014). Moreover, consumers create a significant association between their personality, in terms of characteristics and preferences, and the trademark of the brand they are using (Nyadzayo & Khajehzadeh, 2016). Khan, Jadoon, and Tareen (2016) argued that the traditional advertising media such as TV, radio and magazines have positive effects on brand awareness and brand loyalty. Although the importance of SM marketing on consumer decision-making towards brands has been researched, the influence of social network on conscious consumers towards a brand is still not clear (Hudson, Huang, Roth, & Madden, 2016). Nowadays, consumers are dependent on social media channels to follow up and interact with the fashion and branding domain. In fact, marketing strategies that could motivate consumers to share brand photos and videos might be a good strategy to gain followers’ purchase intention towards the brand and engage them in group discussions, which could enhance the reputation of a brand (Mukherjee & Banerjee, 2017). Therefore, we proposed that social network marketing has an impact on the construct of brand conscious.

The impact of social network on consumers’ BL behaviour in previous studies is so limited (Aro, Suomi, & Saraniemi, 2018). The root of the construct definition of BL can be referred to two theories: the triangular theory of love (Rubin, 1970) and interpersonal theory of love (Sarkar, Ponnam, & Murthy, 2012). According to Langner, Bruns, Fischer, and Rossiter (2016), BL reflects the extent to which consumers have a strong emotional relationship with a certain brand. Proksch, Orth, and Cornwell (2015) argued that to build a strong relationship between consumers and brand, it is essential to do so by building positive feelings and attitudes for the brand on different social media platforms. The emoji symbols like smiley faces or red heart are the consumers’ way to show their feelings and love towards the brand (Lee & Hong, 2016). Therefore, we hypothesize that social media marketing positively affects the construct of brand love.

Value Consciousness

Lee and Hong (2016) stated that symbolic status and prestige are not the only drivers for consumers to acquire a brand’s product. In some situations, consumers prefer to pay low price to some quality. Those consumers are classified as value-conscious consumers (Shirai, 2015).

Based on the earlier studies, value-conscious consumers like to compare a product’s benefits with its cost before making the decision to buy (Delgado-Ballester, Hernandez-Espallardo, & Rodriguez-Orejuela, 2014).

Ahn and Back (2018) argued that cognitive loyalty is shaped by positive attitudes, beliefs and perception towards the product brand. Diallo, Coutelle-Brillet, Riviere, and Zielke (2015) found that the value-conscious consumers have less interest to be loyal to a brand unless the benefits are higher than the cost. Thus, it is assumed that value consciousness has a negative relationship with a cognitive brand loyalty.

The concept of value conscious has been used in advertising and customer behavioural studies as a predictor of AL. The higher the level of consumers’ VC, the lower the level of loyalty towards a specific brand since it is easier to switch to another brand once they can get the same product at a lower price (Ferreira & Coelho, 2015). Delgado-Ballester et al. (2014) argued that different marketing communication strategies have less impact on high value-conscious consumers to motivate them to repurchase a product brand because they have a strong relationship with the brand. Accordingly, it is hypothesized that value consciousness construct has an optimistic impact on AL.

Matthews et al. (2014) defined the term conative loyalty as a future purchase behaviour towards a specific brand or goods. Joshi and Rahman (2015) have already confirmed that perceived value is considered a critical motivator of consumer repurchase behaviour towards a product brand. Therefore, value-conscious consumers mostly select product brand that satisfies their needs at low cost. Ferreira and Coelho (2015) and Tingchi Liu, Brock, Cheng Shi, Chu, and Tseng (2013) stated that consumers who are always looking for a product brand that is reasonably priced would likely to repurchase the same product in future, as long as the product is still within the cost that the consumers prefer. Thus, the study puts forth the following argument: value-conscious construct has a destructive impact on the construct of conative loyalty.

Brand Consciousness

Brand-conscious consumers are defined as consumers whose mental process is focused on selecting well-known brands’ products (Wolter, Brach, Cronin Jr, & Bonn, 2016). In addition, these consumers have different levels of BC and are classified based on consumption behaviour (Yi-Cheon Yim, Sauer, Williams, Lee, & Macrury, 2014). Moreover, these customers evaluate product quality through the brand name, which might have an impact on the purchase decision process (Nikhashemi, Valaei, & Tarofder, 2017). Therefore, brand-conscious customers are more prejudiced by the perceived related knowledge they have about a brand and its information source (Ferreira & Coelho, 2015). The more varied the sources available about a brand’s product, the bigger the attraction for consumers to be loyal to the brand. Thus, it is assumed that BC would positively influence cognitive brand loyalty. Brand consciousness construct has a positive impact on the construct of cognitive brand loyalty.

Brand-conscious consumers are attached to a certain brand because they believe that acquiring the brand reflects their status symbols and prestige (Raj & Roy, 2015). Moreover, these consumers are looking for benefit that is beyond functional benefits which would lead to enhance their status in their social community (Dessart, Veloutsou, & Morgan-Thomas, 2015). People would consider to purchase a product brand in the future when a strong emotional relationship has been formed and developed (Albert & Thomson, 2018). According to Sasmita and Mohd Suki (2015), brand-conscious consumers are more likely to be attached to a brand easily and rarely switch to other competing brands. In this level, loyalty is created not because of perceived functional values but because of the emotional and symbolic values (Wolter et al., 2016). Furthermore, consumers, because of their emotional relationship with a brand, would most likely form positive and favourable attitude and beliefs towards a brand that could increase the possibility to buy a product brand in future. Thus, it is assumed that BC would positively influence affective brand loyalty (AL); BC positively has an impact on conative brand loyalty (CNL).

Brand Love

Wallace, Buil, and de Chernatony (2014) defined BL as a consumer–brand relationship with positive emotions and attitudes towards the brand. Sasmita and Mohd Suki (2015) argued that the extensive consumer–brand relationship helps to create more sustainable brands and improve brand loyalty. Prior studies have confirmed that brand attachment, which refers to a strong, positive, emotional relationship with a brand, leads to consumers’ feeling of love for a brand (Wallace et al., 2014). Huang (2017) stated that companies can maintain an emotional relationship between customers and their brand by understanding how the consumer gets attached to a brand, which eventually leads to build consumer’s loyalty towards a specific brand. Although the link between BL and brand loyalty has been confirmed in previous studies, the link between BL and different stages of loyalty is still not clear. Therefore, the hypothesis is: BL positively has an impact on cognitive brand loyalty, affective brand loyalty and conative brand loyalty.

Brand Loyalty

Customer brand loyalty has been defined in different studies as customers’ repurchase intents towards a particular brand instead of other brands and their commitments to purchase the brand (Nyadzayo & Khajehzadeh, 2016).

According to Oliver (1999), behavioural loyalty refers to repurchase frequency of the products. However, this type of loyalty could not measure the actual level of loyalty towards a particular brand as there are many customers who repurchase products because they needed them but actually dislike these products like the products in supermarkets; as a result, behavioural loyalty might not give a clear picture of actual customer loyalty towards the brand (Nyadzayo & Khajehzadeh, 2016). On the other hand, the attitude of customers towards specific brand is known as attitudinal loyalty. Several studies indicated that attitudinal loyalty includes commitment and intention to purchase the brand (Srivastava & Kaul, 2016; Yoshida, Heere, & Gordon, 2015). Kang, Tang, and Lee (2015) suggested that brand loyalty includes three stages, with each stage relying on achieving the previous stage. The first stage is cognitive loyalty, which occurs when consumers’ perceptions are shaped by past information about the brand that they have and the level of their expectation that should be met (TaghiPourian & Bakhsh, 2015). The second stage is known as AL. In this stage, customers have a strong emotional attachment with the brand, which results in positive attitude and good experience that lead customers to be satisfied with a brand (Lee, Manthiou, Jeong, Tang, & Chiang, 2015). Keeping customers satisfied leads to conative loyalty. Literature confirms the sequences of brand loyalty stages that start with cognitive up to conative loyalty, which are supported by information processing theory (Herrero, San Martín, Garcia de los Salmones, & Collado, 2017; Tybout, Calder, & Sternthal, 1981). Thus, the study proposes the following argument: cognitive brand loyalty construct has an optimistic impact on the construct of affective brand loyalty; affective brand loyalty construct has an optimistic impact on the construct of conative brand loyalty.

Based on the literary discussion thus far, this study proposes a framework as shown in Figure 1.

Objectives and Rationale of the Studies

Recently, the fast fashion industry has significantly evolved. Many brands have changed their business model to adopt the fast fashion model, which forced companies to adopt the latest fashion trend to align with consumers’ demand at a reasonable price (Bhardwaj & Fairhurst, 2010). A study indicated that consumer behaviour towards fast fashion is not fully understood and explored, and they recommended implementing empirical research to understand consumer behaviour towards fast fashion. It is very important to investigate how brand loyalty is developed through different stages for consumers who have different types of consciousness such as BC and VC (Zhang et al., 2014).

Therefore, this research attempts to examine the relation between SM marketing and brand loyalty stages through VC, BC and BL. The study also focuses to highlight the role of SM and its relationship with VC, BC and BL, which eventually create a stronger customer loyalty towards a fast fashion brand.

Figure 1.

Source: The authors.

Research Method

This research employs a questionnaire as the main data collection instrument. The questions are adapted from literature review, and Appendix A shows all the proposed questions and its origins from previous studies.

The questionnaire was initially designed in English language and then translated to Malay, the local language of Malaysia. Adler (1983) stated that translation has to be done by experts who are proficient in the two languages; therefore, an official English–Malay translator was assigned to carry out the translation. Answering the survey in either English or Malay was allowed when collecting the data because both the languages are used in daily life. The population of the study is consumers who frequently buy fast fashion goods in Malaysia, which is considered as a huge population because Malaysia population is more than 30 millions (Bhardwaj & Fairhurst, 2010; Blázquez, 2014); therefore, convenience sampling is used to collect data. To add on, data have been collected from potential consumers shopping at any of the well-known fast fashion brands such as these following six brands: Zara, H&M, Mango, Top Shop, Forever 21 and Uniqlo at Pavilion, Suria-KLCC and The Gardens Mall by trained facilitators (Euromonitor, 2018; Su & Chang, 2018). Target participants were identified after they have left the fast fashion brand retailers with evidence of purchase from these retailers to ensure that they have purchased fast fashion products in the last 5 years. Furthermore, there is a screening question in the questionnaire to make sure the survey’s respondents are consumers who make frequent purchase of fashion products, from any of the known six fast fashion brands in Malaysia, and actively interact or follow the social media of fast fashion brands.

A pilot test was employed with a sample size of 50 students at one of the universities in Malaysia before collecting the actual data.

Sample Size

As the study is using SEM analysis techniques, the minimum sample size (Hair, Hult, Ringle, & Sarstedt, 2016) and effective sample size (Cohen, 1992) rules are applied to satisfy the requirement of analysis. Therefore, the calculation for the minimum required sample size is based on the number of items and constructs besides the interrelation between the components (Westland, 2010). Prior research has shown that sample size ranges between 40 and 500 is considered an effective size (Hair, Ringle, & Sarstedt, 2011). In addition, according to Cohen (1992), the effective sample size is 146 samples. Therefore, the collected sample of 240 respondents is acceptable.

Structural Equation Modelling

PLS–SEM analysis includes two methods that evaluate internal design (measurement version) along with exterior design (structural version) (Henseler et al., 2015). The measurement assessments validate the connection between the items and constructs, while the structural assessments assess the connection between constructs (hypothesis testing) (Anderson & Gerbing, 1988). In the current study, PLS–SEM examination is evaluated by SmartPLS application.

Results of the Study

Descriptive Analysis on Sample Profile

The collected data set have been cleaned to remove no-fitted and no-completed answers, and the final data set for analysis is 240 samples. Table 1 shows the distribution of genders is 57.1 per cent of women and 42.9 per cent of men. For the age characteristic, the largest group of participants (48.2%) are those between 20 and 30 years old. As for the education level, the majority of respondents (69.6%) are studying for bachelor’s degree, followed by 20.4 per cent of respondents who are studying diploma. For race characteristics, most of the participants are Malays (46.3%) followed by Indians (25.4%).

Table 1.

Demographic Characteristics Analysis

Variable	Frequency	Percentage
Gender
Female	137	57.1
Male	103	42.9
Age (years)
17–19	20	8.3
20–30	202	84.2
31–40	11	4.6
41–50	6	2.5
>50	1	0.4
Education background
SPM level	2	0.8
Diploma	49	20.4
Bachelor degree	167	69.6
Master level	6	2.5
PhD level	12	5.0
Other	4	1.7
Ethnicity
Malay	111	46.3
Chinese	33	13.8
Indian	61	25.4
Other	35	14.6
Income-level RM
<1,000	166	69.2
1,001–2,000	47	19.6
2,001–3,000	5	2.1
3,001–4,000	8	3.3
4,001–5,000	5	2.1
>5,000	9	3.8

Source: The authors.

Assessing Structural Model Reliability and Validity

Construct Validity

It is common to start the examination of a measurement model by performing two assessments of outer loading and cross-loading scores of the different items to examine the internal reliability and discriminant reliability. These assessments are to make sure that every item has enough loading, above 0.708, in its associated construct, which is higher than any other loading in the foreign constructs. Table 2 shows that all the proposed items have adequate loading in its associated construct and have enough distance from other constructs.

Table 2.

Loadings and Cross-loadings

	AL	BC	BL	CgL	CnL	SM	VC
AL1	0.907	0.452	0.756	0.680	0.676	0.427	0.063
AL2	0.894	0.457	0.703	0.630	0.669	0.417	0.051
AL3	0.889	0.475	0.658	0.725	0.696	0.327	−0.001
BC1	0.375	0.780	0.393	0.402	0.362	0.232	−0.057
BC2	0.381	0.745	0.404	0.326	0.296	0.232	0.284
BC3	0.431	0.841	0.429	0.477	0.392	0.378	−0.016
BC4	0.441	0.810	0.486	0.540	0.445	0.233	−0.094
BL1	0.640	0.460	0.804	0.628	0.599	0.404	0.145
BL2	0.652	0.433	0.839	0.608	0.575	0.350	0.149
BL3	0.676	0.467	0.859	0.626	0.608	0.366	0.049
BL4	0.661	0.482	0.866	0.619	0.606	0.394	0.066
BL5	0.681	0.410	0.833	0.581	0.605	0.386	0.024
BL6	0.644	0.464	0.824	0.653	0.695	0.320	−0.071
CgL1	0.675	0.510	0.692	0.803	0.639	0.363	0.160
CgL2	0.601	0.506	0.572	0.869	0.670	0.306	−0.053
CgL3	0.626	0.471	0.643	0.871	0.676	0.291	−0.070
CgL4	0.679	0.425	0.615	0.880	0.680	0.321	−0.037
CnL1	0.704	0.446	0.721	0.662	0.853	0.368	0.053
CnL2	0.693	0.393	0.665	0.672	0.875	0.391	0.027
CnL3	0.590	0.373	0.521	0.644	0.847	0.302	−0.092
CnL4	0.530	0.377	0.523	0.635	0.773	0.303	−0.050
SM1	0.439	0.360	0.377	0.357	0.400	0.812	0.107
SM2	0.366	0.301	0.374	0.326	0.338	0.818	0.111
SM3	0.280	0.228	0.314	0.233	0.262	0.777	0.252
SM4	0.255	0.155	0.315	0.248	0.275	0.730	0.177
VC1	0.012	−0.002	0.036	0.001	−0.031	0.121	0.816
VC2	−0.036	−0.054	0.014	−0.048	−0.051	0.126	0.781
VC3	0.075	0.087	0.095	0.069	0.030	0.229	0.908
VC4	0.047	−0.025	0.061	−0.054	−0.021	0.156	0.856

Source: The authors.

Notes: AL, affective brand loyalty, CnL, conative brand loyalty; CgL, cognitive brand loyalty; BL, brand love; SM, social media marketing activities; VC, value consciousness BC, brand consciousness.

Bold characters are the loadings value for the respective construct.

Reliability and Convergent Validity

The present research analyzed the amount of Cronbach’s alpha value to evaluate the reliability and internal consistency of the data. In the evaluation of Cronbach’s alpha, the value more than 0.70 is considered a proper measure, which demonstrates internal consistency (Hair et al., 2016). The reliability of the current study is recognized for the whole constructs, which is given in Table 3. We investigate the construct validity by analyzing composite reliability, discriminant validity and convergent validity. Reliability values higher than 0.7 for all the constructs confirm the internal consistency. Convergent validity investigated by means of outer loading and the average variance. Outer loadings higher than 0.5 are considered significant and display convergence validity. Correspondingly, the average variance extracted (AVE) among a set of constructs should be more than 0.50 to present proper convergent validity. The AVE and composite reliability for the current study tabulated in Table 3 demonstrate adequate convergent validity. There is no collinearity problem between variables, as the VIF value at the acceptable level is between 0.2 and 5.0.

Table 3.

Analysis of Structural Model Reliability and Validity

Construct	Item	Loading	AVE	VIF	Cronbach’s Alpha	Composite Reliability
Social media marketing activities (SM)	SM1	0.812	0.616	1.658	0.793	0.865
	SM2	0.817		1.733
	SM3	0.777		1.591
	SM4	0.729		1.510
Value consciousness (VC)	VC1	0.815	0.708	2.067	0.866	0.906
	VC2	0.780		1.832
	VC3	0.907		2.260
	VC4	0.856		2.191
Brand consciousness (BC)	BC1	0.779	0.631	1.686	0.806	0.872
	BC2	0.745		1.598
	BC3	0.840		1.852
	BC4	0.810		1.667
Brand love (BL)	BL1	0.804	0.701	2.069	0.914	0.933
	BL2	0.838		2.529
	BL3	0.858		2.876
	BL4	0.865		2.898
	BL5	0.832		2.550
	BL6	0.824		2.362
Cognitive brand loyalty (CgL)	CgL1	0.803	0.733	1.713	0.8783	0.9165
	CgL2	0.869		2.463
	CgL3	0.870		2.644
	CgL4	0.879		2.712
Affective brand loyalty (AL)	AL1	0.907	0.804	2.591	0.8785	0.925
	AL2	0.894		2.433
	AL3	0.889		2.274
Conative brand loyalty (CnL)	CnL1	0.852	0.701	2.253	0.846	0.846
	CnL2	0.874		2.426
	CnL3	0.847		2.356
	CnL4	0.772		1.939

Source: The authors.

Discriminant Validity

Table 4 shows the Fornell–Larcker criterion matrix, which proves that discriminant validity assessment is achieved because the measure within any column or row is not exceeding the value in the diagonal (Byrne, 1998; Hair et al., 2016). Another essential examination for discriminant validity is the heterotrait–monotrait correlations (HTMT) ratio, which has been recommended by Henseler, Ringle, and Sarstedt (2015). Table 5 shows that all the HTMT values are acceptable because none of the results show the value of 1.

Table 4.

Fornell–Larcker Criterion Matrix (Discriminant Validity)

Construct	AL	BC	BL	CgL	CnL	SM	VC
Affective brand loyalty	0.897
Brand consciousness	0.514	0.794
Brand love	0.786	0.540	0.837
Cognitive brand loyalty	0.756	0.559	0.739	0.856
Conative brand loyalty	0.758	0.476	0.734	0.778	0.837
Social media marketing activities	0.434	0.341	0.441	0.375	0.411	0.785
Value consciousness	0.041	0.018	0.070	0.003	−0.01	0.200	0.841

Source: The authors.

Notes: Values in the diagonal (bold) represent the square root of the AVE, while the off-diagonals represent the correlations.

AL, affective brand loyalty; CnL, conative brand loyalty; CgL, cognitive brand loyalty; BL, brand love; SM, social media marketing activities; VC, value consciousness and BC, brand consciousness.

Table 5.

Heterotrait–Monotrait (HTMT) Ratio of Correlations (Discriminant Validity)

	Original Sample (O)	Sample Mean (M)	2.5%	97.5%
AL → CnL	0.459	0.457	0.300	0.597
BC → AL	0.042	0.045	−0.056	0.144
BC → CgL	0.224	0.223	0.073	0.366
BC → CnL	0.053	0.051	−0.035	0.150
BL → AL	0.490	0.489	0.350	0.634
BL → CgL	0.622	0.622	0.499	0.736
BL → CnL	0.349	0.350	0.215	0.506
CgL → AL	0.371	0.369	0.215	0.503
SM → BC	0.342	0.351	0.228	0.475
SM → BL	0.442	0.449	0.337	0.565
SM → VC	0.200	0.201	0.018	0.341
VC → AL	0.005	0.005	−0.074	0.075
VC → CgL	−0.044	−0.045	−0.123	0.036
VC → CnL	−0.056	−0.057	−0.133	0.021

Source: The authors.

Notes: AL, affective brand loyalty; CnL, conative brand loyalty; CgL, cognitive brand loyalty; BL, brand love; SM, social media marketing activities; VC, value consciousness and BC, brand consciousness.

One of the complementary tests is the confirmatory tetrad analysis (CTA). This test has been proposed by Gudergan et al. (2008) to ensure whether the measurement model in PLS is reflective or formative. The idea is to generate pairs of covariance between the indicators of the same construct; if all the construct tetrads are non-significant, then the construct is reflective model. On the other hand, if any of the tetrads is significant, then the construct is formative model. As shown in Table 6, all the p-values associated with the tetrads are above the threshold value of 0.05. Therefore, all the constructs are modelled as reflective relations with its indicators.

Structural Model Findings

This present study runs the PLS−SEM algorithm to analyze the data. As illustrated in Figure 2, the hypothesized relationships among the variables were proposed.

The score of R² is representing the predictive power of dependent variable determination because it is the rate of variance explained in the dependent variable as a result of its predictors. According to Hair et al. (2016), R² can have a value between 0 and 1, which shows the percentage of variance explanation in variables as a result of the antecedents’ variables. R² is interpreted as strong if above 0.75, moderate if between 0.50 and 0.75, and weak if between 0.20 and 0.50.

As shown in Figure 2, brand loyalty variances are explained by VC, BC and BL, and in total, the model can explain 58.5 per cent, 68.8 per cent and 63 per cent of cognitive, affective and conative, respectively. Predictive relevance, Q², is another value associated with R² to predict the relevance of the endogenous variables. Q² can be acquired by performing the blindfolding procedure, and the values are small if between 0.02 and 0.15, medium if between 0.15 and 0.35, and large if above 0.35 (Hair et al., 2016). The measures of Q² for the three variables of loyalty are large with the values of 0.426, 0.544 and 0.437, respectively.

One of the examinations to assess the impact level of every construct on the outcome variables is the effective size. The effective size can be calculated based on the predictive power R² to estimate ƒ² scores or based on the predictive relevance Q² to estimate q² scores. Cohen (1992) sets the rule of thumb in interpreting the results, in which 0.02, 0.15 and 0.35 are the margins between small, medium and large effect. Table 7 shows the scores of ƒ², in which the construct brand loyalty has the highest effective size on the three outcome variables, followed by brand conscious then value conscious. Table 8 shows the scores of q², in which the construct brand loyalty has the highest effective size on the three outcome variables, followed by brand conscious then value conscious. Both q² and ƒ² have similar scores’ trend which are mapped with the results of the path coefficient estimates as discussed in the later findings.

Table 6.

Confirmatory Tetrad Analysis of the Measurement Model

BC (four Indicators)	Path Coefficient	T-statistics (\|O/STDEV\|)	p-Values
1. BC1, BC2, BC3, BC4	0.031	1.229	0.126
2. BC1, BC2, BC4, BC3	0.036	1.436	0.115
BL (six indicators)
1. BL1, BL2, BL3, BL4	0.003	0.311	0.756
2. BL1, BL2, BL4, BL3	0.009	0.934	0.351
4. BL1, BL2, BL3, BL5	0.016	1.938	0.055
6. BL1, BL3, BL5, BL2	−0.004	0.499	0.617
7. BL1, BL2, BL3, BL6	−0.003	0.278	0.781
10. BL1, BL2, BL4, BL5	0.009	1.213	0.225
16. BL1, BL2, BL5, BL6	0.028	1.905	0.057
22. BL1, BL3, BL4, BL6	0.011	1.173	0.241
26. BL1, BL3, BL6, BL5	0.005	0.344	0.731
CgL (four indicators)
1. CgL1, CgL2, CgL3, CgL4	0.025	1.504	0.113
2. CgL1, CgL2, CgL4, CgL3	0.026	1.532	0.126
CnL (four indicators)
1. CnL1, CnL2, CnL3, CnL4	0.025	1.399	0.112
2. CnL1, CnL2, CnL4, CnL3	0.088	1.951	0.052
SM (four indicators)
1. SM1, SM2, SM3, SM4	0.065	1.614	0.107
2. SM1, SM2, SM4, SM3	0.071	1.253	0.214
VC (four indicators)
1. VC1, VC2, VC3, VC4	0.014	0.581	0.561
2. VC1, VC2, VC4, VC3	0.003	0.112	0.911

Source: The authors.

Notes: AL, affective brand loyalty, CnL, conative brand loyalty; CgL, cognitive brand loyalty; BL, brand love; SM, social media marketing activities; VC, value consciousness and BC, brand consciousness.

The findings of the hypothesized associations are tabulated in Table 9. It shows that H1, H2, H3, H7, H10, H11, H12, H13 and H14 are supported except for H4, H5, H6, H8 and H9. The rejected hypotheses represent the relationships of VC with cognitive (Cgl), affective (AL), and conative brand loyalty (CnL) as well as BC with AL and CnL. Hypotheses H1–H3 are about the association between social network marketing and VC, BC and BL, which are supported because the t-statistics measure is above the cut-statistics measure of 1.96. The related t-statistics measures are 2.458, 5.239 and 7.482, and the related path coefficient measures are 0.200, 0.342 and 0.442, respectively. Hypotheses H4–H6 consider the association between VC and the three loyalty constructs. The relations are surprisingly rejected because t-statistics measures of 1.037, 0.128 and 1.448, respectively, are below the threshold of 1.96. Perhaps, consumers who are value conscious are difficult to be loyal towards a specific brand especially when investigated in the fast fashion industry. These consumers find fast fashion industry as always changing in terms of design, style and price. As a result, they will not be loyal unless there are other factors that might build loyalty. Hypothesis H7 assumes the association between BC and CgL. With the t-statistic measure of 3.012 and path coefficients of 0.224, the hypothesis is accepted. However, hypotheses H8 and H9 which propose the relationships between AL and CnL are rejected, as the t-statistics measures are 0.813 and 1.087, respectively. BC provides consumers with knowledge of a certain brand, which lead them to cognitive loyalty but not to affective or conative loyalty. However, in a fast fashion context, Malaysian consumers will not be emotionally attached to a brand or even fully trust and commit themselves to regularly buy from a specific fast fashion brand. In fact, both nature of the industry and consumers’ characteristics have strong influences on consumers’ loyalty. Hypotheses H10–H12 propose the association between BL and three constructs of loyalty. The relationships are supported as the t-statistics measures are 10.057, 6.449 and 4.751, and the path coefficient measures are 0.622, 0.0.490 and 0.349, respectively. Hypothesis H13 deliberates the association between CgL and AL, which is supported as the t-statistics measure is 5.010, and the coefficient path is 0.371. Finally, hypothesis H14, which considers the association between AL and CnL, is supported as the t-values measure is 5.797, and the path coefficient measure is 0.459.

Figure 2.

Source: The authors.

Table 7.

Effective Size f² Estimations

	CgL				AL				CnL
	R² Included	R² Excluded	ƒ² Value	Status	R² Included	R² Excluded	ƒ² Value	Status	R² Included	R² Excluded	ƒ² Value	Status
SM	0.585	0.587	−0.005	No effect	0.688	0.688	0.000	No effect	0.630	0.628	0.005	Slight
VC	0.585	0.584	0.002	Slight	0.688	0.688	0.000	No effect	0.630	0.628	0.005	Slight
BC	0.585	0.550	0.084	Small	0.688	0.687	0.003	Slight	0.630	0.628	0.005	Slight
BL	0.585	0.313	0.655	Large	0.688	0.586	0.327	Medium	0.630	0.586	0.119	Medium
CgL					0.688	0.631	0.183	Medium	0.630	0.629	0.003	Slight
AL									0.630	0.556	0.200	Medium

Source: The authors.

Table 8.

Effective Size q² Estimations

	CgL				AL				CnL
	Q² Included	Q² Excluded	q² Value	Status	Q² Included	Q² Excluded	q² Value	Status	Q² Included	Q² Excluded	q² Value	Status
SM	0.426	0.427	−0.003	No effect	0.544	0.543	0.001	Slight	0.437	0.437	0.001	Slight
VC	0.426	0.423	0.004	Slight	0.544	0.545	−0.003	No effect	0.437	0.435	0.004	Slight
BC	0.426	0.395	0.052	Small	0.544	0.543	0.001	Slight	0.437	0.424	0.024	Small
BL	0.426	0.229	0.342	Medium	0.544	0.465	0.171	Medium	0.437	0.408	0.052	Small
CgL					0.544	0.499	0.098	Small	0.437	0.425	0.022	Small
AL									0.437	0.381	0.100	Small

Source: The authors.

The population have different categories based on the demographic characteristics. For example, based on the gender characteristic, the dataset has two groups: male and female. The tested relations can be different between groups. Table 10 shows the path coefficient scores of the two groups, the differences and the significance level (p-value and t-value). From the 14 relations within the model, 12 relations have no significant difference between male and female, and 2 relations have a significant difference. Social media impact on value conscious is higher for females than males; the path coefficient scores of female and male groups are 0.384 and 0.61, respectively. In addition, the value-conscious impact on conative brand loyalty is higher for females than for males; the path coefficient score of female and male group is 0.038 and −0.016, respectively. The two results show that females brand loyalty can be resultant from the influence of social media use.

Table 9.

Measurement Relationships and Hypothesis Testing

Hypothesis	Path	Path Coefficient (b)	T-statistics	p-Values	Decision
H1	SM → VC	0.200	2.458**	0.014	Supported
H2	SM → BC	0.342	5.239***	0.000	Supported
H3	SM → BL	0.442	7.482***	0.000	Supported
H4	VC → CgL	−0.044	1.037	0.300	Not Supported
H5	VC → AL	0.005	0.128	0.898	Not Supported
H6	VC → CnL	−0.056	1.448	0.148	Not Supported
H7	BC → Cgl	0.224	3.012***	0.003	Supported
H8	BC → AL	0.042	0.813	0.416	Not Supported
H9	BC → CnL	0.053	1.087	0.278	Not Supported
H10	BL → CgL	0.622	10.057***	0.000	Supported
H11	BL → AL	0.490	6.449***	0.000	Supported
H12	BL → CnL	0.349	4.751***	0.000	Supported
H13	CgL → AL	0.371	5.010***	0.000	Supported
H14	AL → CnL	0.459	5.797***	0.000	Supported

Source: The authors.

Discussion

The main objective of the current study is to identify the factors which play a major role in consumers’ loyalty behaviour. Therefore, this research attempts to extend the previous studies on loyalty behaviour through examining the role of social network marketing (SM) on VC, BC and BL and how these variables affect the phases of loyalty, cognitive, affective and conative within the fast fashion industry in Malaysia. The present study supports the idea that SM has an important role in marketing which can help marketers to understand BL, BC and VC in influencing consumers’ behaviour. The result of this study could help marketers understand the effective communication strategy that can be adopted through social media to shape positive attitudes.

Table 10.

Multi Group Analysis (MGA) of Gender Groups

	Path Coefficients Original (female)	STDEV (female)	Path Coefficients Original (male)	STDEV (male)	Path Coefficients-diff (\|male - female\|)	t-Value (male vs. female)	p-Value (male vs. female)
AL → CnL	0.577	0.118	0.393	0.100	0.183	1.140	0.256
BC → AL	0.110	0.062	−0.044	0.086	0.154	1.497	0.136
BC → CgL	0.122	0.081	0.318	0.112	0.195	1.456	0.147
BC" CnL	0.025	0.076	0.067	0.068	0.043	0.404	0.686
BL" AL	0.585	0.082	0.381	0.109	0.204	1.535	0.126
BL → CgL	0.722	0.068	0.495	0.099	0.227	1.957	0.052
BL → CnL	0.214	0.117	0.422	0.093	0.208	1.326	0.186
CgL → AL	0.257	0.077	0.489	0.108	0.231	1.806	0.072
SM → BC	0.346	0.085	0.341	0.100	0.005	0.042	0.967
SM → BL	0.461	0.075	0.423	0.086	0.038	0.334	0.739
SM → VC	0.384	0.078	0.061	0.119	0.323	2.365	0.019
VC → AL	−0.008	0.045	0.020	0.062	0.028	0.372	0.710
VC → CgL	−0.003	0.058	−0.093	0.052	0.090	1.116	0.266
VC → CnL	0.038	0.059	−0.160	0.049	0.198	2.475	0.014

Source: The authors.

According to Berezan, Yoo, and Christodoulidou (2016), traditional advertising tools such as TV, stereo and magazines were generally adopted as marketing communications on brand name loyalty, brand connection as well as brand recognition. However, today’s consumers tend to be more conscious of using social network to easily find details about brands of interests as compared with the traditional tools of marketing communications. Nowadays, following fashion trends and brands is so much easier due to the availability of details of merchandise on social media platforms. Hence, social network marketing is a good strategy for brand awareness and trustworthy source for brand-conscious consumers. It has informative and interactive features. The contribution of this study’s findings would be the extension of advertising communication tools to social networking and understanding its impact on BC users.

The outcomes also prove that perceived social Internet marketing activities have an impact on BL. Brands that allow customers to express themselves on social networking sites, as an example, tend to be loved (Wallace et al., 2014). People who are much more engaged with almost all social networking activities, for instance by liking, posting and talking about the articles of a particular brand name, are more likely to be emotionally attached to a manufacturer, which leads to feeling of love towards the manufacturer. Thus, marketers need to develop methods to interact and connect with clients through various activities on social media because it could improve the love of shoppers for a brand name.

However, the hypothesized relationships between VC and brand loyalty stages are not supported, which is inconsistent with the findings of Ismail (2017) who found that value-conscious consumers tend to be brand loyal consumers. Nevertheless, in the previous study, brand name loyalty was viewed as an individual construct, and the analysis was on social media users without focusing on any specific industry. In comparison, this study focuses on the fast fashion industry, and the findings reveal that people who are value conscious would not be loyal and are often purchasing from just any fast fashion brand. This particular category of customers found more value whenever they change from brand to brand as long as they could find cheaper price and spend less time and effort to purchase a fast fashion product.

In comparison, the hypothesized connection between cognitive loyalty and BC is supported even though the connections between BC and conative and affective brand loyalty are not. Based on Wallace et al.’s (2014) results, people who are brand conscious and like to own recognized brands tend to be more dedicated to a brand. People who want to buy a product, particularly high involvement products, might begin with looking for information externally or internally, which may improve their knowledge with regard to the brand, and then select or loyally buy the item. Nevertheless, as a result of the dynamics of the merchandise, fast fashion might lead consumers to choose or even continue purchasing the item due to the information available on a merchandise’s brand; however excessive BC wouldn’t affect their happiness (AL) or even their dedication to purchase a specific item brand (conative loyalty). On the other hand, this study has revealed that if customers discover information that is valuable with regard to some other brands, and if these information meets the customers’ expectations over the competitor’s product, powerful customer satisfaction might develop (affective brand loyalty), which leads to the customer selecting or perhaps continuing to purchase the item (conative brand loyalty).

Theoretical and Practical Implications

The theoretical contribution of this study lies in creating a conceptual framework for building brand loyalty towards fast fashion products in the Malaysian context. Additionally, it provides empirical evidence to support earlier contentions proposed by various other scholars. Although brand loyalty continues to be examined in different industries (Giovanis, Athanasopoulou, & Tsoukatos, 2016; Khan, Rahman, & Fatma 2016; Vernuccio et al., 2015), not one of them has analyzed the effect of BL, BC, and VC on every loyalty component. Thus, combining each variable and analyzing them in one model has produced a clear point of view and also affords a multidimensional image of causal relationship between the variables which lead to building brand loyalty in the fast fashion market.

For practical contribution, this study established new variables for industry segmentation by highlighting the benefits of social networking marketing activities as well as other strategies that might work with targeting market segment whether the customers are value conscious or may be brand conscious. According to Fournier and Yao (1997), managers have to understand different levels of consumers’ loyalty, which can reflect various levels of commitment towards a company and response differently to the proposed advertising strategy.

In this particular research, cognitive loyalty is displayed as a crucial starting place of customer brand loyalty. At this point, consumers assess the valuation of the item according to the hedonic benefits since their BC is higher than their VC. Thus, marketers need to engage clients in the social networking activities. The bigger the engagement of clients on the social media, by clearly showing the hedonic values of the merchandise, the higher the level of BC, which may lead to strong dedication towards the company with the competing brand.

In comparison, people who are in the AL phase are not usually influenced by BC or VC. These customers have self-expressive needs to fulfil their self-definitional demands (Kressmann et al., 2006), Consequently, they are inclined to be affected by mental stimulus and even by other inner things, for instance mental arousal, in addition to societal significance (Kang, Liu, & Kim, 2013). People who are mentally attached to a brand, which will create BL, tend to be more dedicated towards a brand. Thus, marketers need to keep a strong relationship with the people by focusing on delivering hedonic experiences and portraying the good, caring and loving aspect of the company’s value through image advertising or maybe advertising plan.

The conative loyalty phase could only be recognized by marketers when they obtain good responses from customers towards their brand name. The customer who reaches this stage shows solid commitment and trust towards the company (Oliver, 1999). Those type of customers should be acknowledged by marketers and rewarded for their loyalty.

Limitations and Future Research Direction

This particular analysis contributes to the body of knowledge, both theoretically and practically. Nevertheless, several limitations ought to be emphasized as well as addressed in later studies. For starters, future researchers should look into testing out the hypotheses in different cultures as well as in different industries and societies to further validate the outcomes of this particular study. Second, it will be good for future study to look at age and gender as moderating variables of the connection between social networking activates along with other variables of this research. Third, since the predicting strength of conative loyalty is approximately 63 per cent, this suggests that there is a 37 per cent unexplained variance. As a result, for the benefit of future research, qualitative analysis is actually suggested to identify new variables, which will improve the explanation power of the model.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Acknowledgements

The authors are grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the paper. Usual disclaimers apply.

Appendix

Appendix A.

Research Constructs and Measurement Items

Constructs	Scale	Source
Social network activities (SM)	SM1: I find interesting contents shown in social media about the brand I am considering to buy. SM2: Ii is easy to deliver my opinion about the brand I am considering buying through social media. SM3: Using social media to search for information about the brand I am considering buying is very trendy. SM4: I would like to pass along information on brand, product, or services from social media to my friends	(Kim & Ko, 2012)
Value consciousness (VC)	VC1: I am very concerned about low prices, but I am equally concerned about product quality. VC2: When shopping, I compare the prices of different brands to be sure I get the best value for the money. VC3: When purchasing a product, I always try to maximize the quality I get for the money I spend. VC4: When I buy products, I like to be sure that I am getting my money’s worth.	(Lichtenstein, Ridgway, & Netemeyer, 1993)
Brand consciousness (BC)	BC1: I pay attention to the brand names of the products I buy. BC2: Brand names tell me something about the quality of the products. BC3: Sometimes I am willing to pay more money for products because of its brand name. BC4: Branded products that cost a lot of money are good quality.	(Sprotles & Kendall, 1986)
Brand love (BL)	BL1: The brand XYZ is a wonderful brand. BL2: The brand XYZ makes me feel good. BL3: The brand XYZ is totally awesome. BL4: The brand XYZ makes me very happy. BL5: I’m very attached to XYZ brand BL6: I love XYZ brand.	(Carroll & Ahuvia, 2006)
Cognitive Brand loyalty (CgL)	CgL1: Brand XYZ provides me superior product quality as compared to other competitors in marketplace. CgL2: No other brands perform better than brand XYZ. CgL3: Overall quality of brand XYZ is the best in marketplace CgL4: I believe brand XYZ provides more benefits than other brands in Marketplace.	(Oliver, 1999)
Affective Brand Loyalty (PI)	AL1: I love purchase from brand XYZ. AL2: I feel better when I purchase brand XYZ. AL3: I like brand XYZ more than other competing brands in marketplace.	(Oliver, 1999)
Conative brand loyalty (CnL)	CnL1: If I am given a chance, I intend to continue buying from brand XYZ. CnL2: I consider brand XYZ to be my first choice. CnL3: This is the only brand of this type of product that I will buy. CnL4: When I go shopping, I don’t even notice competing brands.	(Oliver, 1999)

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