The moderating role of age on social media marketing activities and customer brand engagement on Instagram social network

In the context of fashion brands on Instagram platform, this study aims to investigate the impact of age on the relationships between informative, interactive and trendy social media marketing (SMM) activities, customer brand engagement (CBE) and brand loyalty

The moderating role of age on social

media marketing activities and

customer brand engagement on

Instagram social network

Abstract

Purpose – In the context of fashion brands on Instagram platform, this study aims to investigate the

impact of age on the relationships between informative, interactive and trendy social media marketing

(SMM) activities, customer brand engagement (CBE) and brand loyalty.

Design/methodology/approach – A quantitative method was used to collect and analyses the data and

to test the conceptual model In total, 241 usable questionnaires were collected and analyzed using

structural equation modeling and multi-group moderation analysis.

Findings – The results of this study demonstrate that informativeness of SMM activities relates positively

and significantly to CBE in all age groups However, the strength and the significance of interactive and

trendy social media activities differ between age groups.

Research limitations/implications – This study used only two age groups of college students to

answer the research questions Despite that tech-savvy millennials and generation Z are highly engaged

in social media environment, the results may not be representative of the entire population and the

findings may be cautiously generalized to other platform types or product categories.

Originality/value – By offering a new understanding of perceived SMM in different age groups on

Instagram platform, this study contributes to the literature by identifying the types of social media

activities that engage different age groups on social media networks.

Keywords Age, Instagram, Customer engagement, Brand loyalty, Social media marketing,

Fast fashion brands

Paper typeResearch paper

1 Introduction

The marketing literature suggests that customer brand engagement (CBE) is an essential

driver of brand loyalty, positive word of mouth, brand usage and purchasing behaviors

(Algharabat et al., 2020;Brodie et al., 2013;France et al., 2016;Hollebeek et al., 2014)

Within this rich stream of research, social media marketing (SMM) activities were identified

as potent enablers of CBE and brand loyalty (De Vries and Carlson, 2014;Ismail, 2017;

Nyadzayo et al., 2020;Ul Islam and Rahman, 2017) Earlier studies have emphasized the

role of interactive, informative and trendy SMM activities on CBE and loyalty (Godey et al.,

2016; Kim and Ko, 2012; Liu et al., 2019; Yadav and Rahman, 2017) However, these

previous studies used specific online platforms such as Facebook or Twitter, and the results

might not be generalized to other social media networks such as Instagram (Ul Islam and

Rahman, 2017) Besides, the literature did not investigate the moderating role of age on the

relationship between SMM activities and CBE Nevertheless, in social media context, the

Joe Hazzam is based at Faculty of Business and Law, The British University

in Dubai, Dubai, United Arab Emirates.

Received 17 March 2021 Revised 8 May 2021

26 June 2021

16 August 2021 Accepted 16 August 2021

CBE and the use of different social media platforms might differ significantly within diverse age groups (Boardman and McCormick, 2018;Duffett, 2017;Dwivedi, 2015;Nelson et al.,

2019)

In recent years, many firms adjusted to the rise of consumer power by leveraging the interactive capability of social media networks to enhance customer’s value, develop relationships with communities and communicate efficiently with their customers to achieve brand objectives (Hennig-Thurau et al., 2013; Felix et al., 2017) Besides, social media platforms emphasize customer–brand interactions and facilitate involvement through a far-reaching influence on the cognitive, emotional and activation processes of engagement (Hollebeek et al., 2014) The engagement of these customers in a virtual online community support brand connection, value and loyalty (Brodie et al., 2013;France et al., 2015) For example, the Facebook page fans of fashion retailer Zara spend more than non-fans by 138% (Hotspex, 2013) Thus, the identification of social media activities that are relevant and interesting to the brand community members improves their participation, trust, commitment and engagement (Vohra and Bhardwaj, 2019)

Previous studies identified different types of SMM activities as essential drivers of CBE and subsequently brand loyalty and equity (Yazdanparast et al., 2016) For example, the accessibility and the interactivity elements on social media platforms between the brands and their millennial customers fulfill their emotional and cognitive needs (Athwal et al.,

2019) On the other hand, entertainment, interactive and trendiness of SMM activities are important enablers of consumers’ brand engagement, preference and loyalty toward luxury fashion brands (Godey et al., 2016; Kim and Ko, 2012) These studies advanced the knowledge toward a better understanding of the relationships between SMM activities, CBE and loyalty However, the exclusivity and the premium prices of luxury brands differ from the fast fashion brands which are more affordable and produced in higher volumes (Ajitha and Sivakumar, 2019) Besides, previous studies used Facebook and Twitter as the primary social networks for analysis which might differ from other context and interface designs such as Instagram platform (Ul Islam and Rahman, 2017) Moreover, previous studies did not investigate how different age groups impact the relationship between SMM activities and CBE

Studies that investigate the impact of perceived social media activities on CBE focused mainly on millennials using Twitter and Facebook social media platforms (Kim and Ko,

2012; Samala and Katkam, 2019; Ul Islam and Rahman, 2017) While these studies advanced the knowledge on social media activities and CBE, their findings inform specific age group and did not consider young consumers whom digital behaviors differ significantly from their millennials counterpart (Bolton et al., 2013; Nguyen et al., 2019) Furthermore, Instagram has unique characteristics, and consumers might perceive the platform’s social media activities differently in comparison to other types such as Facebook and Twitter (Chen, 2017) This study attempts to address these research gaps by investigating how different types of fast fashion SMM activities are effective in engaging customers from different age groups on Instagram social networks Also, this study focus

on the Middle East region, and specifically the United Arab Emirates which might provide new knowledge on the attitude of consumers toward social media in developing countries Thus, the research aims to answer the primary question: “Does age moderate the relationship between SMM activities and customer brand engagement towards fast fashion brands?” This research question is grounded in the stimulus-organism-response paradigm (Eroglu et al., 2001;Jacoby, 2002) Accordingly, we propose that different types of SMM activities stimulate the consumer as an organism to engage with the brand leading to a response such as brand loyalty However, the organism is the storehouse of prior experiences and attitudes which is influenced by the individual age (Jacoby, 2002) Thus, the impact of SMM stimulus on the organism emotive and cognitive systems might differ between different age groups such as Millennials and generation Z (Duffett, 2017)

First, the objective of the study is to measure the relationships between SMM, CBE and

brand loyalty on Instagram social network of fast fashion brands Second, the research

investigates the age groups differentiated engagement behaviors in response to different

types of SMM activities The following sections highlight the study background, the

research conceptual framework and hypotheses development Then, we present the

method used and the findings Finally, we discuss the theoretical and managerial

implications, limitations of the study and the recommendations for future research

2 Research background

In recent years, the performance of fast fashion brands, such as Zara and H&M, explained

impressive global performance (Kim et al., 2013) These brands business models are

defined as agile, highly responsive to latest trends and changes in consumer lifestyle, and

their prices are reasonable (Su and Chang, 2018) Fast fashion brands represent a quick

shift to the new emerging trends of consumer’s demands These brands highlight efficient

production processes and maintain lower prices (Gabrielli et al., 2013) Besides, the

complementary effect of enhanced design and quick response production matched the

supply to demand and impacted the consumer purchasing behaviors (Cachon and

Swinney, 2011) These customers explain the fast fashion environment as creative and

associated with fun, enjoyment and pleasure (Miller, 2013) The fast fashion brands

approach of offering a variety of alternatives and affordable prices attract young and more

mature consumers (Bhardwaj and Fairhurst, 2010) Thus, the continuous engagement with

customers is an essential success factor of fast fashion brands which focus on customer

demands within a time-constrained environment (Payne, 2016)

Previous studies posit that social media platforms facilitate higher level of customer–brand

interactions, and the communication is faster and happens in real-time with a higher

number of customers (Hamilton et al., 2016; Rathore et al., 2016) These interactions

enhance CBE and contribute positively to brand loyalty and value (Dwivedi, 2015;France

et al., 2016;Hollebeek et al., 2014) Prior research investigated the influence of different

types of SMM activities on CBE using either Facebook or Twitter of luxury fashion brands

(Kim and Ko, 2012;Nyadzayo et al., 2020;Ul Islam and Rahman, 2017) However, empirical

findings on the impact of age on the relationships between SMM and CBE remain a gap in

the literature, especially for fast fashion brands on Instagram platforms Recent statistics

have highlighted that more than 60% of Instagram users are aged between 18 and 34 years

old, and fashion is one of top user interests suggesting higher ads spend by fast fashion

brands on this platform (Facebook, 2021;Statista, 2021) Thus, as shown in the research

conceptual model (Figure 1), the purpose of this research is to understand the impact of

age on the relationship between SMM activities and CBE, and to test the relationship

between CBE and brand loyalty of fast fashion brands on Instagram social media platform

3 Theoretical background

Kaplan and Haenlein (2010, p 61) define social media as: “a group of Internet-based

applications that build on the ideological and technological foundations of Web 2.0, and

that allow the creation and exchange of User Generated Content.” The effective use of SMM

facilitates co-creation of value and enhances customers’ interactions and relationships

(Felix et al., 2017) However, perception and receptivity of SMM exert significant roles in

changing consumer behaviors and engagement with the brands (Hollebeek et al., 2014;

Kumar et al., 2016;Ul Islam and Rahman, 2017) Social media environments differ from

traditional media and represents a new kind of organism that is stimulated by marketing

inputs to achieve marketing outcomes (Peters et al., 2013) This conceptualization relates to

the stimulus-organism-response framework that posits an arousal influence of stimulus on

the organism cognitive and affective psychological state to produce an outcome (Eroglu

et al., 2001) Stimulus consist of the external things that encounter the customers such as

brands, logo, communications and other factors Organism refers to the internal process and activation of the stimulus Response underlines nonverbal, verbal and behavioral responses (Jacoby, 2002) In this study, marketing inputs explain the characteristics of SMM stimulus such as informative, interactive or trendy activities (Kim and Ko, 2012;Yadav and Rahman, 2017) These communication messages stimulate the organism psychological state represented by customer engagement with the brand to achieve a response such as loyalty (Algharabat et al., 2020; Hollebeek et al., 2014) Besides, these psychological processes interact with other customer physical systems such as demographics including age among other variables (Jacoby, 2002)

4 Literature review and hypotheses development 4.1 Social media marketing and customer brand engagement

Social media platforms become an indispensable part of young consumers’ daily life for communications and interactions with their friends or preferred brands (Duffett, 2017) These online social networks change the traditional way of communication, information acquisition, customer relationship and value creation (Kumar et al., 2016;Stephen, 2016; Trainor, 2012) According toHanna et al (2011), social media interactive communication and tools revolutionize the marketing ecosystem and provide brands’ community pages the opportunity to interact and engage with their customers through exchange of information and knowledge (Samala and Katkam, 2019) Accordingly, SMM represents the stimulus that arouse customers’ internal state and facilitates involvement through a far-reaching influence

on the cognitive, emotional and activation processes of engagement (Brodie et al., 2013;Ul Islam and Rahman, 2017) Previous studies outline that social media communications influence: consumers’ level of processing thought in a specific interaction with the brand referring to cognitive engagement, consumers’ degree of positive affect toward the brand explaining emotional engagement and consumers’ level of energy, effort and time interacting with the brand highlighting the activation process (Hollebeek et al., 2014; Samala and Katkam, 2019)

Social media tools accommodate different types of brand stimulus and drive engagement through multiple creative strategies (Ashley and Tuten, 2014) SMM activities on Instagram might be interactive, informative and trendy (Godey et al., 2016;Yadav and Rahman, 2017) For instance, SMM interactive activities refer to the extent customers share content, views and exchange information with the brand messages and other customers (Ul Islam and Rahman, 2017; Yadav and Rahman, 2017) These interactive messages explain that the brand is listening and responding promptly to customers (Bozkurt et al., 2020) According

to Loureiro et al (2019), the interactivity of fast fashion brands on Instagram empowers consumers to engage with the brands and participate in decision-making process Therefore, interactive brand stimulus activates two-way communication and enhances customers’ engagement for value creation (France et al., 2016;Merrilees, 2016):

H1 Interactivity of SMM activities is positively related to CBE.

SMM activities that deliver accurate, useful and comprehensive information might be perceived as informative (Yadav and Rahman, 2017) The information in the message stimulates consumers’ exposure and attention predicting engagement with the brands (Shareef et al., 2019) On the other hand, the visual information presented with audio-visual

or video format generates higher response on Instagram (Kusumasondjaja, 2019) For example, the information, news and promotion posted on Zara Instagram account trigger the users to engage with the brand (Nedra et al., 2019) These findings were confirmed by

Yazdanparast et al (2016) suggesting that SMM informative activities influence CBE by stimulating a two-way conversation and reinforcing brand associations Therefore, the informative brand messages that are relevant and valuable drive customer engagement (Ul Islam and Rahman, 2017)

H2 Informativeness of SMM activities is positively related to CBE.

Trendiness refers to the perceived SMM activities that provide trendy content (Kim and Ko,

2012; Yadav and Rahman, 2017) This trendy content informs consumers about latest

fashion ideas and serves as a stimulus to engage with the brand and make better purchase

decisions (Godey et al., 2016) Trendy activities keep consumers updated about social

environments and inspire them with new and creative ideas that stimulate their engagement

with the brand (Godey et al., 2016) These trendy SMM activities stimulate customers

seeking a sense of uniqueness and style which enhance their level of engagement with the

brand (Ajitha and Sivakumar, 2019)

H3 Trendiness of SMM activities is positively related to CBE.

4.2 Customer brand engagement and brand loyalty

The customer levels of energy, effort and time spent during a specific direct online

interaction with the brand refer to CBE, and this engagement might add value to the brand

beyond economic transactions (Brodie et al., 2011; Hollebeek et al., 2014; Vivek et al.,

2012) The development of a positive relationship with the brand enhances customer

engagement and increases the intention of brand usage, positive word of mouth and

customers’ constructive feedback (Labrecque, 2014) Customers that are more engaged

with brands are more likely to interact, create value and tend to be brand loyal (France

et al., 2015;Hollebeek, 2011; Van Doorn et al., 2010) Previous studies established the

relationship between customer engagement and loyalty (Pansari and Kumar, 2017) For

instance,Algharabat et al (2020) found a positive relationship between CBE and brand

loyalty using the Facebook fan page of mobile phone service providers On the other hand,

Ul Islam and Rahman, (2017) tested this relationship in online brand communities and

confirmed that SMM stimulates higher level of CBE which in turn generates a response such

as brand loyalty (De Vries and Carlson, 2014;Jacoby, 2002) Thus, the following hypothesis

is proposed:

H4 CBE is positively related to brand loyalty.

4.3 Moderation hypothesis

Age is an essential demographic factor that affects the consumption and purchase

decisions of fashion brands (Ajitha and Sivakumar, 2019) For instance, brand, style, price

and social identity influence the millennial attitude and purchase decisions of fashion

apparel (Valaei and Nikhashemi, 2017) Besides, the customer’s motivation, engagement

and shopping behaviors differ within age groups (Sharma et al., 2019) For example, the

Mcommerce is the preferred shopping channel for younger generations owing to the

convenience, selection and exploration However, the enjoyment and the convenience of

the physical stores remained popular for the 60þ years old (Boardman and McCormick,

2018) These differences in customer engagement and shopping behaviors between age

groups were augmented by the emergence of social media (Duffett, 2017) For instance,

the millennials spent their whole lives in a digital context and relies mostly on technology to

engage with their preferred brands (Nadeem et al., 2015) The millennials and the younger

generation Z are the most engaged consumers on social media platforms and specifically

with fashion brands on Instagram social network (Bolton et al., 2013;Casalo et al., 2018;

Nyadzayo et al., 2020) Thus, age represents a strong determinant of social media

engagement and usage (Pittman and Reich, 2016) According toNelson et al (2019), the

millennials use Instagram to seek fashion information and interact with their preferred

brands On the other hand,Priporas et al (2019) argue that the generation Z are more

socially connected than previous generations, and their usage of social media is extensive

which might lead to different engagement behaviors as compared to other generations

These differences in social media usage between age groups might provide insights on

how to better connect and communicate with customers (Yazdanparast et al., 2016) Therefore, the differences in experiences, attitudes and perceptions between Millennials and generation Z might influence the relationship between SMM different types of stimulus and engagement (Duffett, 2017;Jacoby, 2002) This study aims to understand the customer engagement with SMM activities of fast fashion brands by including both millennials and generation Z Thus, the following hypotheses are proposed (Figure 1):

H5a Age moderates the relationship between the interactivity of SMM activities and

CBE

H5b Age moderates the relationship between the informativeness of SMM activities and

CBE

H5c Age moderates the relationship between the trendiness of SMM activities and CBE.

5 Research methodology 5.1 Sampling and data collection

The study used an online survey to collect data from undergraduate and postgraduate students at a university located in United Arab Emirates The target respondents are students that represent the tech-savvy millennials and the extensively engaged generation Z in social media environment (Nadeem et al., 2015; Priporas et al., 2019) Besides, these two generations are major consumers of fast fashion brands, and they engage notably in online brand communities (Bolton et al., 2013;Nelson et al., 2019) The survey started with a filtering question technique to validate that the respondents follow a fast fashion brand on Instagram The students answered the question: “Do you follow at least one of the fast fashion brands listed below on Instagram?” The list of fast fashion brands was developed by reviewing previous literature (Ajitha and Sivakumar, 2019;Miller, 2013) The fast fashion brands identified are Zara, H&M, Topshop, Gap, Diesel, Benetton, Armani exchange, Superdry, Forever 21 and Jack & Jones Then, only those participants that follow a fast fashion brand on Instagram are able to complete the questionnaire Moreover, the participants were asked to think about the fast fashion brands that they follow on Instagram while answering the survey

The survey was sent to a target of 650 students A total of 241 participants successfully completed the questionnaire with 102 (42.3%) of the respondents aged between 18 and 24 and represented the generation Z Besides, 139 (57.7%) of the participants were aged between 25 and 34 and represented the generation Y In terms of gender, the female respondents were 145 (60.2%) and the male participants were 96 (39.8%) The majority of the respondents (173, 71.8%) were using social media for more than 3 h per day.Table 1 provides the characteristics of the respondents

Figure 1 Research conceptual framework and hypotheses

5.2 Measurement development

The literature review supported the development of the questionnaire items and scales for

data collection The study aims to measure the moderating role of age on the relationship

between SMM activities and CBE and to test the relationship between CBE and brand

loyalty A seven-point Likert scale was used to measure all the study variables The SMM

activities items and scale were adapted fromKim and Ko (2012)andYadav and Rahman

(2017)and consist of three items that measure the interactivity, three items that measure the

informativeness and three items that measure the trendiness of SMM activities The CBE

items and scale were adapted fromHollebeek et al (2014)and consist of three items that

measure the cognitive processing, four items that measure the affective and three items that

measure the activation dimension of CBE The brand loyalty items and scale were adapted

fromAlgharabat et al (2020)and consist of three items.Table 2shows the results of the

measurement items and constructs’ psychometric properties

6 Data analysis and results

6.1 Preliminary analysis and measurement model

The data was collected using the same questionnaire during the same period Therefore, we

tested for the existence of common method bias using Harman’s single factor test which could

inflate or deflate the estimates between the latent constructs (Bagozzi and Youjae, 1988) The

common method bias exists if only one factor explains 50% of the variance among the

measures during the exploratory factor analysis with unrotated factor solutions (Podsakoff

et al., 2012) In our study, the highest variance explained by a single factor was 45.52% As

this is less than the 50% cut-off criteria, it confirms the absence of the common method bias

Moreover, results of Kaiser-Meyer-Olkin analysis (0.90) confirm the data adequacy as it

exceeds the 0.60 requirement (Kaiser, 1970) Also, we tested for nonresponse bias by

comparing early and late respondents’ differences across means of the main study constructs

following the recommendation ofArmstrong and Overton (1977) The Levene’s test for equality

of variance indicates that no significant differences exist between early and late respondents

for interactive SMM (F = 0.27, p > 0.05), informative SMM (F = 1.72, p > 0.05), trendy SMM (F

= 0.36, p > 0.05), CBE (F = 0.33, p > 0.05) and brand loyalty (F = 3.84, p > 0.05).

The results of the initial exploratory factor analysis (EFA) explained seven components, and

all the items loaded above the threshold of 0.50 (Hair et al., 2014) The confirmatory factor

analysis (CFA) follows the EFA and indicates how well the measured variables represent the

research constructs (Anderson and Gerbing, 1988;Gallagher et al., 2008) The CFA results

indicate that the measurement model showed adequate goodness-of-fit indices (x2 =

288.639, x2/df = 1.169, p < 0.001, normed fit index [NFI] = 0.950, comparative fit index

[CFI] = 0.992, Tucker–Lewis index [TLI] = 0.991 and root mean square error of

Table 1 Characteristics of respondents

Age

Gender

Male

Social media use per day

Less than 3 h

3–4 h

More than 4 h

68 79 94

28.2 32.8 39.0

approximation [RMSE]) = 0.027).Table 2shows that the values of the standardized factors’ loading estimates were higher than 0.7 with statistical significance and without any loadings above 1 or below 1 Besides, the Cronbach’s alpha showed values higher than the recommended 0.7 (Churchill, 1979)

6.2 Convergent and discriminant validity

Convergent validity refers to the common shared variance between the indicators of the same construct, and discriminant validity evaluates the construct’s divergence and how it differs from others and not measuring the same thing (Gallagher et al., 2008) To assess convergent validity, the composite reliability (CR) and the average variance extracted (AVE) were calculated According to research standards, the CR should be greater than 0.70 and the AVE should be greater than 0.5 (MacKenzie et al., 2011;Fornell and Larcker, 1981) Table 3shows that the values of the AVE were greater than 0.5 and CR above 0.7 indicate convergent validity (Anderson and Gerbing, 1988) Discriminant validity is achieved if the squared root of the AVE of any two constructs is higher than their correlation estimate Table 3shows that the square root AVE of the seven study constructs in the diagonal is greater than their correlations below the diagonal line (Fornell and Larcker, 1981) Thus, the research constructs achieve discriminant validity

Table 2 Measurement items, standardized factor loadings and Cronbach’s alpha coefficients

1 My fast fashion brand enables content sharing with others on Instagram

2 My fast fashion brand interacts regularly with its followers and fans on Instagram

3 My Fast fashion brand facilitates two way interaction with others on Instagram

0.873 0.873 0.841

2 My fast fashion brand offers useful information on Instagram

3 The information provided by my fast fashion brand on Instagram is comprehensive

Trendiness

1 Contents visible on my fast fashion brand Instagram page is the latest trend

2 Using my fast fashion brand Instagram page is really trendy

3 Anything trendy is available on my fast fashion brand Instagram page

CBE cognitive processing

1 Using my fast fashion brand’s Instagram page get me to think about it

2 I think about my fast fashion brand’s Instagram page a lot when I’m using it

3 Using my fast fashion brand’s Instagram page stimulate my interest to learn more about it

CBE affection

1 I feel very positive when I use my fast fashion brand’s Instagram page

2 Using my fast fashion brand’s Instagram page makes me happy

3 I feel good when I use my fast fashion brand’s Instagram page

4 I’m proud to use my fast fashion brand’s Instagram page

CBE activation

1 I spend a lot of time using my fast fashion brand’s Instagram page compared to other brands

2 Whenever I’m using fast fashion brands on Instagram, I usually use my fast fashion brand’s Instagram page

3 I use my fast fashion brand’s Instagram page a lot

Brand loyalty

1 I consider myself to be loyal to my fast fashion brand’s Instagram page

2 My fast fashion brand’s Instagram page would be my first choice

3 I intend to remain a customer to my fast fashion brand on its Instagram page

0.914 0.818

0.867 0.908 0.851

0.829 0.873 0.803

0.895 0.920 0.943 0.869

0.876 0.923 0.909

0.951 0.960 0.939

0.906

0.872

0.948

0.929

0.965

Notes:a: Cronbach’s alpha coefficient; SFLs: standardized factor loadings

6.3 Structural model results

The second stage of structure equation modeling is the path analysis and hypothesis

testing The AMOS software was used to investigate the relationships between interactivity,

informativeness, trendiness, CBE and brand loyalty The CBE was taken as second-order

construct with three factors at the first order in the structural model Besides, the first

structural model did not count for the moderation effects of age groups This procedure is

followed by a multi-group path analysis, which is suitable for testing the study hypotheses

by comparing specific path parameters across the two age groups (Stephenson et al.,

2006) The structural model highlighted acceptable fit indices (x2= 4.749, x2/df = 1.583,

p < 0.001, NFI = 0.992, CFI = 0.997, TLI = 0.972 and RMSEA = 0.049).

Table 4indicates that SMM interactivity relates positively and significantly to CBE (b =

0.146, t = 2.205, p = 0.027), thus supporting H1 Besides, H2 was supported as the

relationship between SMM informativeness and CBE is positive and significant (b = 0.239,

t = 3.335, p < 0.001) In support of H3, SMM trendiness explained a positive and significant

relationship with CBE (b = 0.314, t = 4.814, p < 0.001) Finally, H4 was supported as CBE

is related positively and significantly to brand loyalty (b = 0.430, t = 6.682, p < 0.001).

6.4 Multi-group moderation analysis

Prior to multi-group moderation analysis, we test for invariance by specifying a full

constrained model set to be equal across all age groups Then, the model was compared to

a freely estimated parameters model The groups are different at the model level if the x2

difference test is significant (Byrne, 2010) The results show that Dx2(12) = 19.298 (p =

0.082), and the age groups are different at the model level Thus, the study proceeds to

multi-group moderation path analysis The model showed acceptable fit indices (x2 =

20.364, x2/df = 2.036, p < 0.001, NFI = 0.968, CFI = 0.982, TLI = 0.899 and RMSEA =

0.066) Further, we performed multi-group moderation tests using the critical ratio for

differences in AMOS to compare if there are significant path differences between different

types of SMM activities and CBE in different age groups.Table 5shows that the relationship

between SMM interactivity and CBE is not moderated by age groups (z-score = 0.016,

Table 4 Results of the structural equation modeling

No Hypothetical relationships Path coefficient t-value Result

Notes:
p < 0.05 level;

p < 0.01;


p < 0.001

Table 3 Convergent and discriminant validity

1 CBE cognitive processing 4.993 1.239 0.873 0.835

Notes: CR: construct reliability, SD: standard deviations, Square root AVE: average variance extracted is the diagonal number in italic

p > 0.05) despite that this relationship was positive and significant for generation Z (b = 0.142, p = 0.073) with a confidence level of 90% as opposed to generation Y (b = 0.140,

p = 0.168) Thus, hypothesis H5a was not supported Besides, the relationship between

SMM informativeness and CBE was positive and significant for the two generations

However, age did not moderate this relationship (z-score = 0.428, p > 0.05) and H5b was

rejected On the other hand, the relationship between SMM trendiness and CBE was

significantly moderated by age groups (z-score = 3.426, p < 0.05) This relationship is significant and positive for the generation Z (b = 0.537, p < 0.001) and not significant for generation Y (b = 0.113, p = 0.113) supporting H5c In a post hoc analysis that follows the

same procedure of x2invariance test and multi-group path analysis for female and male groups, the result shows that gender did not moderate the relationships between the different types of SMM activities and CBE

7 Discussion

This research attempts to understand the impact of SMM activities of fast fashion brands on CBE in different age groups on Instagram The study shows that generation Y and generation

Z engage similarly with interactive and informative SMM activities Millennials are interactive and informative social being seeking and exchanging information with their preferred brands (Samala and Katkam, 2019) However, the trendiness of SMM is affecting positively and significantly the engagement of generation Z specifically The result is consistent with the premises of the stimulus-organism-response framework explaining that SMM activities stimulate CBE which in turn has subsequent effect on brand loyalty (Ul Islam and Rahman,

2017) On the other hand, this study contributes to the engagement literature by proposing that social media stimulus is not consistent across generation Y and Z members The impact

of these activities differs according to the organism age highlighting the influence of customer’s physical system and their engagement with brand’s social media stimulus (Jacoby, 2002) The moderation analysis indicates that younger consumers relate more to new ideas and latest information that inspire them to engage and stay loyal to the brand This finding confirms that these consumers engage and build relationships with brands differently from their millennials counterpart (Duffett, 2017) This result addresses a gap in the literature

on how different age groups perceived SMM activities of fast fashion brands on Instagram, and how these types of online communications influence their CBE and loyalty

Previous research found that brand communities on Facebook and Twitter positively influence customer–brand relationship and engagement (Gensler et al., 2013;Godey et al.,

2016) Also, SMM activities enhance communication and improve CBE and loyalty (De Vries and Carlson, 2014;Sashi, 2012) This research confirmed and extended previous findings

to Instagram social network and found that interactivity, informativeness and trendiness of SMM activities relate positively and significantly to CBE that in turn, can contribute to brand loyalty Thus, this result is in line with previous studies that found a positive and significant relationship between SMM activities and CBE (Ul Islam and Rahman, 2017; Yadav and Rahman, 2017) and more specifically on social media platforms of luxury fashion brands (Kim and Ko, 2012;Nyadzayo et al., 2020)

Table 5 Moderation analysis– generation Y vs generation Z

No Hypotheses

Path coefficients (b ) – generation Y

Path coefficients (b ) – generation Z z-score Result

H5b Informativeness! CBE 0.312
0.246

0.428 Not supported

H5c Trendiness! CBE 0.138 0.537


3.426


Supported Notes:
p < 0.05 level;

p < 0.01;


p < 0.001